diff --git a/otbenchmark/master/_downloads/018bebf23004eb4c852157b01b94c189/plot_borgonovo_sensitivity.ipynb b/otbenchmark/master/_downloads/018bebf23004eb4c852157b01b94c189/plot_borgonovo_sensitivity.ipynb
new file mode 100644
index 00000000000..63ff75b7401
--- /dev/null
+++ b/otbenchmark/master/_downloads/018bebf23004eb4c852157b01b94c189/plot_borgonovo_sensitivity.ipynb
@@ -0,0 +1,235 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the Borgonovo test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.BorgonovoSensitivity()\nprint(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the function\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "marginal_distribution = ot.ComposedDistribution(\n    [\n        ot.KernelSmoothing().build(full_sample.getMarginal(i))\n        for i in range(1 + dimension)\n    ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform sensitivity analysis\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
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diff --git a/otbenchmark/master/_downloads/13b9169f4ad20d57745bd3f8f0cb6282/plot_benchmark_sensitivity_methods.ipynb b/otbenchmark/master/_downloads/13b9169f4ad20d57745bd3f8f0cb6282/plot_benchmark_sensitivity_methods.ipynb
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index 00000000000..fc04b1ce438
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+++ b/otbenchmark/master/_downloads/13b9169f4ad20d57745bd3f8f0cb6282/plot_benchmark_sensitivity_methods.ipynb
@@ -0,0 +1,365 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark sensitivity analysis methods\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## When we estimate Sobol' indices, we may encounter the following warning messages:\nWRN - The estimated first order Sobol index (2) is greater than its total order index ...\nWRN - The estimated total order Sobol index (2) is lesser than first order index ...\n```\nLots of these messages are printed in the current Notebook. This is why we disable them with:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.Log.Show(ot.Log.NONE)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use Borgonovo problem\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.BorgonovoSensitivity()\ndistribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nexact_total_order = problem.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Saltelli estimator with Monte-Carlo sample\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sample_size = 10000"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "inputDesign = ot.SobolIndicesExperiment(distribution, sample_size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(\n    inputDesign, outputDesign, sample_size\n)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", sample_size)\n# First order\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Saltelli estimator with Quasi Monte-Carlo sample\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sample_size = 500"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nsequence = ot.SobolSequence(dimension)\nrestart = True\nexperiment = ot.LowDiscrepancyExperiment(sequence, distribution, sample_size, restart)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "inputDesign = ot.SobolIndicesExperiment(experiment).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(\n    inputDesign, outputDesign, sample_size\n)\nfirst_order = sensitivityAnalysis.getFirstOrderIndices()\ntotal_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", sample_size)\n# First order\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Loop over the estimators\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Available estimators:\")\nestimators_list = otb.SensitivityBenchmarkMetaAlgorithm.GetEstimators()\nfor sobolAlgorithm in estimators_list:\n    name = sobolAlgorithm.getClassName()\n    print(\" - \", name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "metaSAAlgorithm = otb.SensitivityBenchmarkMetaAlgorithm(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Monte-Carlo sampling\")\nfor sobolAlgorithm in estimators_list:\n    (\n        computed_first_order,\n        computed_total_order,\n    ) = metaSAAlgorithm.runSamplingEstimator(sample_size)\n    name = sobolAlgorithm.getClassName()\n    print(name)\n    print(\"    S = \", computed_first_order)\n    print(\"    T = \", computed_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Quasi Monte-Carlo sampling\")\nfor estimator in [\"Saltelli\", \"Martinez\", \"Jansen\", \"MauntzKucherenko\"]:\n    (\n        computed_first_order,\n        computed_total_order,\n    ) = metaSAAlgorithm.runSamplingEstimator(\n        sample_size, estimator=estimator, sampling_method=\"QMC\"\n    )\n    name = sobolAlgorithm.getClassName()\n    print(name)\n    print(\"    S = \", computed_first_order)\n    print(\"    T = \", computed_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Polynomial chaos\")\nsample_size = 500\n(\n    computed_first_order,\n    computed_total_order,\n) = metaSAAlgorithm.runPolynomialChaosEstimator(\n    sample_size_train=sample_size,\n    sample_size_test=2,\n    total_degree=5,\n    hyperbolic_quasinorm=0.5,\n)\nprint(\"    S = \", computed_first_order)\nprint(\"    T = \", computed_total_order)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define the metric\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We consider the following accuracy metrics:\n* the vector or log relative errors for a given index (first order or total order),\n* the mean log relative error, as the mean of the LRE vector (first order or total order),\n* the average mean log relative error, as the mean of the first and total order mean log relative error.\n\nLarger LRE values are prefered.\n\nThe first order (resp. total order) mean LRE represents the mean number of digits for all components\nof the first order indices (resp. total order indices).\nThe average mean LRE represents the mean LRE for both first and total order indices.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "S_LRE = ot.Point(dimension)\nT_LRE = ot.Point(dimension)\nfor i in range(dimension):\n    S_LRE[i] = otb.ComputeLogRelativeError(\n        computed_first_order[i], exact_first_order[i]\n    )\n    T_LRE[i] = otb.ComputeLogRelativeError(\n        computed_total_order[i], exact_total_order[i]\n    )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"LRE S = \", S_LRE)\nprint(\"LRE T = \", T_LRE)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mean_LRE_S = sum(S_LRE) / dimension\nmean_LRE_T = sum(T_LRE) / dimension\nmean_LRE = (mean_LRE_S + mean_LRE_T) / 2.0\nprint(\"Mean LRE S = %.2f\" % (mean_LRE_S))\nprint(\"Mean LRE T = %.2f\" % (mean_LRE_T))\nprint(\"Mean LRE = %.2f\" % (mean_LRE))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The digit per point ratio measure the number of digits relatively to the sample size. A greater value is prefered.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "digit_per_point_ratio = mean_LRE / sample_size\nprint(\"Digit / point = %.3e\" % (digit_per_point_ratio))"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/otbenchmark/master/_downloads/15180aff63b94e352470b9af8a67b807/plot_flood_sensitivity.py b/otbenchmark/master/_downloads/15180aff63b94e352470b9af8a67b807/plot_flood_sensitivity.py
new file mode 100644
index 00000000000..7c6bc90ee00
--- /dev/null
+++ b/otbenchmark/master/_downloads/15180aff63b94e352470b9af8a67b807/plot_flood_sensitivity.py
@@ -0,0 +1,95 @@
+"""
+Benchmark the Flooding test function
+====================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.FloodingSensitivity()
+
+# %%
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+exact_first_order
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+exact_total_order
+
+# %%
+# Plot the function
+# -----------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+full_sample = ot.Sample(size, 1 + dimension)
+full_sample[:, range(dimension)] = inputDesign
+full_sample[:, dimension] = outputDesign
+full_description = list(inputDesign.getDescription())
+full_description.append(outputDesign.getDescription()[0])
+full_sample.setDescription(full_description)
+
+# %%
+marginal_distribution = ot.ComposedDistribution(
+    [
+        ot.KernelSmoothing().build(full_sample.getMarginal(i))
+        for i in range(1 + dimension)
+    ]
+)
+clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+_ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)})
+
+# %%
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# Perform sensitivity analysis
+# ----------------------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 10000
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/177f69a01b2091371b1393b3784ee85e/plot_convergence_reliability_mc.zip b/otbenchmark/master/_downloads/177f69a01b2091371b1393b3784ee85e/plot_convergence_reliability_mc.zip
index f3140bda73c..71700da848a 100644
Binary files a/otbenchmark/master/_downloads/177f69a01b2091371b1393b3784ee85e/plot_convergence_reliability_mc.zip and b/otbenchmark/master/_downloads/177f69a01b2091371b1393b3784ee85e/plot_convergence_reliability_mc.zip differ
diff --git a/otbenchmark/master/_downloads/39c987f12c082b6f6c2b1c199aed0518/plot_flood_sensitivity.ipynb b/otbenchmark/master/_downloads/39c987f12c082b6f6c2b1c199aed0518/plot_flood_sensitivity.ipynb
new file mode 100644
index 00000000000..1974475fc3d
--- /dev/null
+++ b/otbenchmark/master/_downloads/39c987f12c082b6f6c2b1c199aed0518/plot_flood_sensitivity.ipynb
@@ -0,0 +1,246 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the Flooding test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.FloodingSensitivity()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the function\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "marginal_distribution = ot.ComposedDistribution(\n    [\n        ot.KernelSmoothing().build(full_sample.getMarginal(i))\n        for i in range(1 + dimension)\n    ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (10.0, 10.0)})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform sensitivity analysis\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/otbenchmark/master/_downloads/3b23190f79b9a4829b68307be5e836d7/plot_ishigami_sensitivity.py b/otbenchmark/master/_downloads/3b23190f79b9a4829b68307be5e836d7/plot_ishigami_sensitivity.py
new file mode 100644
index 00000000000..09864ade9ce
--- /dev/null
+++ b/otbenchmark/master/_downloads/3b23190f79b9a4829b68307be5e836d7/plot_ishigami_sensitivity.py
@@ -0,0 +1,97 @@
+"""
+Benchmark the Ishigami test function
+====================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.IshigamiSensitivity()
+
+# %%
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+exact_first_order
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+exact_total_order
+
+# %%
+# Plot the function
+# -----------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+full_sample = ot.Sample(size, 1 + dimension)
+full_sample[:, range(dimension)] = inputDesign
+full_sample[:, dimension] = outputDesign
+full_description = list(inputDesign.getDescription())
+full_description.append(outputDesign.getDescription()[0])
+full_sample.setDescription(full_description)
+
+# %%
+marginal_distribution = ot.ComposedDistribution(
+    [
+        ot.KernelSmoothing().build(full_sample.getMarginal(i))
+        for i in range(1 + dimension)
+    ]
+)
+clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+_ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+# %%
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# Perform sensitivity analysis
+# ----------------------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 10000
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+# Compute absolute error (the LRE cannot be computed,
+# because S can be zero)
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/3bd92b053e8108a3b84f939606e1f584/plot_gaussian_product_sensitivity.ipynb b/otbenchmark/master/_downloads/3bd92b053e8108a3b84f939606e1f584/plot_gaussian_product_sensitivity.ipynb
new file mode 100644
index 00000000000..acb757fb745
--- /dev/null
+++ b/otbenchmark/master/_downloads/3bd92b053e8108a3b84f939606e1f584/plot_gaussian_product_sensitivity.ipynb
@@ -0,0 +1,246 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the gaussian product test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.GaussianProductSensitivity()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nprint(exact_first_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nprint(exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the function\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "marginal_distribution = ot.ComposedDistribution(\n    [\n        ot.KernelSmoothing().build(full_sample.getMarginal(i))\n        for i in range(1 + dimension)\n    ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\nview = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform sensitivity analysis\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/otbenchmark/master/_downloads/3e62dca12ac3db00aaba5db1a12e3867/plot_benchmark_sensitivity_methods.py b/otbenchmark/master/_downloads/3e62dca12ac3db00aaba5db1a12e3867/plot_benchmark_sensitivity_methods.py
new file mode 100644
index 00000000000..4d0ee641f21
--- /dev/null
+++ b/otbenchmark/master/_downloads/3e62dca12ac3db00aaba5db1a12e3867/plot_benchmark_sensitivity_methods.py
@@ -0,0 +1,191 @@
+"""
+Benchmark sensitivity analysis methods
+======================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+
+# %%
+# When we estimate Sobol' indices, we may encounter the following warning messages:
+# ```
+# WRN - The estimated first order Sobol index (2) is greater than its total order index ...
+# WRN - The estimated total order Sobol index (2) is lesser than first order index ...
+# ```
+# Lots of these messages are printed in the current Notebook. This is why we disable them with:
+ot.Log.Show(ot.Log.NONE)
+
+# %%
+# Use Borgonovo problem
+problem = otb.BorgonovoSensitivity()
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+exact_total_order = problem.getTotalOrderIndices()
+
+# %%
+# Saltelli estimator with Monte-Carlo sample
+# ------------------------------------------
+
+# %%
+sample_size = 10000
+
+# %%
+inputDesign = ot.SobolIndicesExperiment(distribution, sample_size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(
+    inputDesign, outputDesign, sample_size
+)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", sample_size)
+# First order
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+# Saltelli estimator with Quasi Monte-Carlo sample
+# ------------------------------------------------
+
+# %%
+sample_size = 500
+
+# %%
+dimension = distribution.getDimension()
+sequence = ot.SobolSequence(dimension)
+restart = True
+experiment = ot.LowDiscrepancyExperiment(sequence, distribution, sample_size, restart)
+
+# %%
+inputDesign = ot.SobolIndicesExperiment(experiment).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(
+    inputDesign, outputDesign, sample_size
+)
+first_order = sensitivityAnalysis.getFirstOrderIndices()
+total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", sample_size)
+# First order
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+# Loop over the estimators
+# ------------------------
+
+# %%
+print("Available estimators:")
+estimators_list = otb.SensitivityBenchmarkMetaAlgorithm.GetEstimators()
+for sobolAlgorithm in estimators_list:
+    name = sobolAlgorithm.getClassName()
+    print(" - ", name)
+
+# %%
+metaSAAlgorithm = otb.SensitivityBenchmarkMetaAlgorithm(problem)
+
+# %%
+print("Monte-Carlo sampling")
+for sobolAlgorithm in estimators_list:
+    (
+        computed_first_order,
+        computed_total_order,
+    ) = metaSAAlgorithm.runSamplingEstimator(sample_size)
+    name = sobolAlgorithm.getClassName()
+    print(name)
+    print("    S = ", computed_first_order)
+    print("    T = ", computed_total_order)
+
+# %%
+print("Quasi Monte-Carlo sampling")
+for estimator in ["Saltelli", "Martinez", "Jansen", "MauntzKucherenko"]:
+    (
+        computed_first_order,
+        computed_total_order,
+    ) = metaSAAlgorithm.runSamplingEstimator(
+        sample_size, estimator=estimator, sampling_method="QMC"
+    )
+    name = sobolAlgorithm.getClassName()
+    print(name)
+    print("    S = ", computed_first_order)
+    print("    T = ", computed_total_order)
+
+# %%
+print("Polynomial chaos")
+sample_size = 500
+(
+    computed_first_order,
+    computed_total_order,
+) = metaSAAlgorithm.runPolynomialChaosEstimator(
+    sample_size_train=sample_size,
+    sample_size_test=2,
+    total_degree=5,
+    hyperbolic_quasinorm=0.5,
+)
+print("    S = ", computed_first_order)
+print("    T = ", computed_total_order)
+
+# %%
+# Define the metric
+# -----------------
+
+# %%
+# We consider the following accuracy metrics:
+# * the vector or log relative errors for a given index (first order or total order),
+# * the mean log relative error, as the mean of the LRE vector (first order or total order),
+# * the average mean log relative error, as the mean of the first and total order mean log relative error.
+#
+# Larger LRE values are prefered.
+#
+# The first order (resp. total order) mean LRE represents the mean number of digits for all components
+# of the first order indices (resp. total order indices).
+# The average mean LRE represents the mean LRE for both first and total order indices.
+
+# %%
+S_LRE = ot.Point(dimension)
+T_LRE = ot.Point(dimension)
+for i in range(dimension):
+    S_LRE[i] = otb.ComputeLogRelativeError(
+        computed_first_order[i], exact_first_order[i]
+    )
+    T_LRE[i] = otb.ComputeLogRelativeError(
+        computed_total_order[i], exact_total_order[i]
+    )
+
+# %%
+print("LRE S = ", S_LRE)
+print("LRE T = ", T_LRE)
+
+# %%
+mean_LRE_S = sum(S_LRE) / dimension
+mean_LRE_T = sum(T_LRE) / dimension
+mean_LRE = (mean_LRE_S + mean_LRE_T) / 2.0
+print("Mean LRE S = %.2f" % (mean_LRE_S))
+print("Mean LRE T = %.2f" % (mean_LRE_T))
+print("Mean LRE = %.2f" % (mean_LRE))
+
+# %%
+# The digit per point ratio measure the number of digits relatively to the sample size. A greater value is prefered.
+digit_per_point_ratio = mean_LRE / sample_size
+print("Digit / point = %.3e" % (digit_per_point_ratio))
diff --git a/otbenchmark/master/_downloads/3f2f5008640b2a9bf5fb56d17e3345fe/plot_gsobol_sensitivity.zip b/otbenchmark/master/_downloads/3f2f5008640b2a9bf5fb56d17e3345fe/plot_gsobol_sensitivity.zip
new file mode 100644
index 00000000000..797b484720b
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diff --git a/otbenchmark/master/_downloads/5d0b8a3ea31fa859435ac0c7fb178822/plot_gaussian_product_sensitivity.py b/otbenchmark/master/_downloads/5d0b8a3ea31fa859435ac0c7fb178822/plot_gaussian_product_sensitivity.py
new file mode 100644
index 00000000000..0c8b93a0ee7
--- /dev/null
+++ b/otbenchmark/master/_downloads/5d0b8a3ea31fa859435ac0c7fb178822/plot_gaussian_product_sensitivity.py
@@ -0,0 +1,97 @@
+"""
+Benchmark the gaussian product test function
+============================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.GaussianProductSensitivity()
+
+# %%
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+print(exact_first_order)
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+print(exact_total_order)
+
+# %%
+# Plot the function
+# -----------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+full_sample = ot.Sample(size, 1 + dimension)
+full_sample[:, range(dimension)] = inputDesign
+full_sample[:, dimension] = outputDesign
+full_description = list(inputDesign.getDescription())
+full_description.append(outputDesign.getDescription()[0])
+full_sample.setDescription(full_description)
+
+# %%
+marginal_distribution = ot.ComposedDistribution(
+    [
+        ot.KernelSmoothing().build(full_sample.getMarginal(i))
+        for i in range(1 + dimension)
+    ]
+)
+clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+view = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+# %%
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# Perform sensitivity analysis
+# ----------------------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 10000
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+# Compute absolute error (the LRE cannot be computed,
+# because S can be zero)
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/5fab7882172318ff3e3ffcf48ef9eddf/plot_ishigami_sensitivity.zip b/otbenchmark/master/_downloads/5fab7882172318ff3e3ffcf48ef9eddf/plot_ishigami_sensitivity.zip
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diff --git a/otbenchmark/master/_downloads/629687dd8581020f5bdda375e12cf702/plot_gaussian_sum.zip b/otbenchmark/master/_downloads/629687dd8581020f5bdda375e12cf702/plot_gaussian_sum.zip
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diff --git a/otbenchmark/master/_downloads/643785dea4d339bb780f3446d3c50609/plot_gsobol_sensitivity.ipynb b/otbenchmark/master/_downloads/643785dea4d339bb780f3446d3c50609/plot_gsobol_sensitivity.ipynb
new file mode 100644
index 00000000000..21be589916a
--- /dev/null
+++ b/otbenchmark/master/_downloads/643785dea4d339bb780f3446d3c50609/plot_gsobol_sensitivity.ipynb
@@ -0,0 +1,253 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the G-Sobol test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.GSobolSensitivity()\nprint(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nprint(exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot function\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "marginal_distribution = ot.ComposedDistribution(\n    [\n        ot.KernelSmoothing().build(full_sample.getMarginal(i))\n        for i in range(1 + dimension)\n    ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 1000\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform SA\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/otbenchmark/master/_downloads/66b4d9d63c28c108996cfe858ca9289e/plot_morris_sensitivity.zip b/otbenchmark/master/_downloads/66b4d9d63c28c108996cfe858ca9289e/plot_morris_sensitivity.zip
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diff --git a/otbenchmark/master/_downloads/6b55e1d416c35886a31041a6892cb0f0/plot_borehole_sensitivity.zip b/otbenchmark/master/_downloads/6b55e1d416c35886a31041a6892cb0f0/plot_borehole_sensitivity.zip
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index 00000000000..e4481c26a39
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diff --git a/otbenchmark/master/_downloads/6e2038d5b1a24a0d43c32c76904fbca5/plot_borehole_sensitivity.py b/otbenchmark/master/_downloads/6e2038d5b1a24a0d43c32c76904fbca5/plot_borehole_sensitivity.py
new file mode 100644
index 00000000000..8384c801bdf
--- /dev/null
+++ b/otbenchmark/master/_downloads/6e2038d5b1a24a0d43c32c76904fbca5/plot_borehole_sensitivity.py
@@ -0,0 +1,95 @@
+"""
+Benchmark the Borehole test function
+====================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.BoreholeSensitivity()
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+exact_first_order
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+exact_total_order
+
+# %%
+# Plot the function
+# -----------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+full_sample = ot.Sample(size, 1 + dimension)
+full_sample[:, range(dimension)] = inputDesign
+full_sample[:, dimension] = outputDesign
+full_description = list(inputDesign.getDescription())
+full_description.append(outputDesign.getDescription()[0])
+full_sample.setDescription(full_description)
+
+# %%
+marginal_distribution = ot.ComposedDistribution(
+    [
+        ot.KernelSmoothing().build(full_sample.getMarginal(i))
+        for i in range(1 + dimension)
+    ]
+)
+clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+_ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)})
+
+# %%
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# Perform sensitivity analysis
+# ----------------------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 10000
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+# Compute absolute error (the LRE cannot be computed,
+# because S can be zero)
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/otbenchmark/master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
index 255aeb352ed..6b23ff1c558 100644
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diff --git a/otbenchmark/master/_downloads/7cff43685509138d3c4a3ae1a2644d62/plot_oakleyohagan_sensitivity.py b/otbenchmark/master/_downloads/7cff43685509138d3c4a3ae1a2644d62/plot_oakleyohagan_sensitivity.py
new file mode 100644
index 00000000000..47c80232989
--- /dev/null
+++ b/otbenchmark/master/_downloads/7cff43685509138d3c4a3ae1a2644d62/plot_oakleyohagan_sensitivity.py
@@ -0,0 +1,96 @@
+"""
+Benchmark the Oakley-O'Hagan test function
+==========================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.OakleyOHaganSensitivity()
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+print(exact_first_order)
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+print(exact_total_order)
+
+
+# ## Plot the function
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+nbcolumns = 5
+nbrows = int(dimension / nbcolumns)
+grid = ot.GridLayout(nbrows, nbcolumns)
+inputDescription = distribution.getDescription()
+outputDescription = model.getOutputDescription()[0]
+index = 0
+for i in range(nbrows):
+    for j in range(nbcolumns):
+        graph = ot.Graph(
+            "n=%d" % (size), inputDescription[index], outputDescription, True, ""
+        )
+        sample = ot.Sample(size, 2)
+        sample[:, 0] = inputDesign[:, index]
+        sample[:, 1] = outputDesign[:, 0]
+        cloud = ot.Cloud(sample)
+        graph.add(cloud)
+        grid.setGraph(i, j, graph)
+        index += 1
+_ = otv.View(grid, figure_kw={"figsize": (10.0, 10.0)})
+
+# %%
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# ## Perform sensitivity analysis
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 1000
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+# Compute absolute error (the LRE cannot be computed,
+# because S can be zero)
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/7f13a44954884297d4db857bec1ecd92/plot_convergence_ishigami.zip b/otbenchmark/master/_downloads/7f13a44954884297d4db857bec1ecd92/plot_convergence_ishigami.zip
index adc3a3b166c..239927d2540 100644
Binary files a/otbenchmark/master/_downloads/7f13a44954884297d4db857bec1ecd92/plot_convergence_ishigami.zip and b/otbenchmark/master/_downloads/7f13a44954884297d4db857bec1ecd92/plot_convergence_ishigami.zip differ
diff --git a/otbenchmark/master/_downloads/82f4b8a8c9c6a4e5332608271aad9cee/plot_dirichlet_sensitivity.ipynb b/otbenchmark/master/_downloads/82f4b8a8c9c6a4e5332608271aad9cee/plot_dirichlet_sensitivity.ipynb
new file mode 100644
index 00000000000..0e549593fa7
--- /dev/null
+++ b/otbenchmark/master/_downloads/82f4b8a8c9c6a4e5332608271aad9cee/plot_dirichlet_sensitivity.ipynb
@@ -0,0 +1,235 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the Dirichlet test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.DirichletSensitivity()\nprint(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the function\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "marginal_distribution = ot.ComposedDistribution(\n    [\n        ot.KernelSmoothing().build(full_sample.getMarginal(i))\n        for i in range(1 + dimension)\n    ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform sensitivity analysis\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/otbenchmark/master/_downloads/86db06e71453f8056914ef0dcd6b0132/plot_nloscillator_sensitivity.ipynb b/otbenchmark/master/_downloads/86db06e71453f8056914ef0dcd6b0132/plot_nloscillator_sensitivity.ipynb
new file mode 100644
index 00000000000..9214561c8af
--- /dev/null
+++ b/otbenchmark/master/_downloads/86db06e71453f8056914ef0dcd6b0132/plot_nloscillator_sensitivity.ipynb
@@ -0,0 +1,235 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the NLOscillator test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.NLOscillatorSensitivity()\nprint(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the function\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "marginal_distribution = ot.ComposedDistribution(\n    [\n        ot.KernelSmoothing().build(full_sample.getMarginal(i))\n        for i in range(1 + dimension)\n    ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (10.0, 10.0)})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform sensitivity analysis\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/otbenchmark/master/_downloads/91f30700716a0dd0a964e349dc44e117/plot_borgonovo_sensitivity.zip b/otbenchmark/master/_downloads/91f30700716a0dd0a964e349dc44e117/plot_borgonovo_sensitivity.zip
new file mode 100644
index 00000000000..7fed79469eb
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diff --git a/otbenchmark/master/_downloads/966aab35daf3bdeee31dc59712af12c7/plot_benchmark_sensitivity_methods.zip b/otbenchmark/master/_downloads/966aab35daf3bdeee31dc59712af12c7/plot_benchmark_sensitivity_methods.zip
new file mode 100644
index 00000000000..13967f29149
Binary files /dev/null and b/otbenchmark/master/_downloads/966aab35daf3bdeee31dc59712af12c7/plot_benchmark_sensitivity_methods.zip differ
diff --git a/otbenchmark/master/_downloads/9aafdbfee23b48eed34e7b7fb324d32a/plot_gaussian_product_sensitivity.zip b/otbenchmark/master/_downloads/9aafdbfee23b48eed34e7b7fb324d32a/plot_gaussian_product_sensitivity.zip
new file mode 100644
index 00000000000..bec7644e012
Binary files /dev/null and b/otbenchmark/master/_downloads/9aafdbfee23b48eed34e7b7fb324d32a/plot_gaussian_product_sensitivity.zip differ
diff --git a/otbenchmark/master/_downloads/9ace8485dedabebe7a1c9e1e1009c0ea/plot_gaussian_sum.py b/otbenchmark/master/_downloads/9ace8485dedabebe7a1c9e1e1009c0ea/plot_gaussian_sum.py
new file mode 100644
index 00000000000..128199fbe9c
--- /dev/null
+++ b/otbenchmark/master/_downloads/9ace8485dedabebe7a1c9e1e1009c0ea/plot_gaussian_sum.py
@@ -0,0 +1,97 @@
+"""
+Benchmark the gaussian sum test function
+========================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.GaussianSumSensitivity()
+
+# %%
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+print(exact_first_order)
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+print(exact_total_order)
+
+# %%
+# Plot the function
+# -----------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+full_sample = ot.Sample(size, 1 + dimension)
+full_sample[:, range(dimension)] = inputDesign
+full_sample[:, dimension] = outputDesign
+full_description = list(inputDesign.getDescription())
+full_description.append(outputDesign.getDescription()[0])
+full_sample.setDescription(full_description)
+
+# %%
+marginal_distribution = ot.ComposedDistribution(
+    [
+        ot.KernelSmoothing().build(full_sample.getMarginal(i))
+        for i in range(1 + dimension)
+    ]
+)
+clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+view = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+# %%
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# Perform sensitivity analysis
+# ----------------------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 10000
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+# Compute absolute error (the LRE cannot be computed,
+# because S can be zero)
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/a707b852149a93fcecac6a74f1fed670/plot_morris_sensitivity.py b/otbenchmark/master/_downloads/a707b852149a93fcecac6a74f1fed670/plot_morris_sensitivity.py
new file mode 100644
index 00000000000..39ba62f891b
--- /dev/null
+++ b/otbenchmark/master/_downloads/a707b852149a93fcecac6a74f1fed670/plot_morris_sensitivity.py
@@ -0,0 +1,98 @@
+"""
+Benchmark the Morris test function
+==================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.MorrisSensitivity()
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+exact_first_order
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+exact_total_order
+
+# %%
+# Plot the function
+# -----------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+nbcolumns = 4
+nbrows = int(dimension / nbcolumns)
+grid = ot.GridLayout(nbrows, nbcolumns)
+inputDescription = distribution.getDescription()
+outputDescription = model.getOutputDescription()[0]
+index = 0
+for i in range(nbrows):
+    for j in range(nbcolumns):
+        graph = ot.Graph(
+            "n=%d" % (size), inputDescription[index], outputDescription, True, ""
+        )
+        sample = ot.Sample(size, 2)
+        sample[:, 0] = inputDesign[:, index]
+        sample[:, 1] = outputDesign[:, 0]
+        cloud = ot.Cloud(sample)
+        graph.add(cloud)
+        grid.setGraph(i, j, graph)
+        index += 1
+_ = otv.View(grid, figure_kw={"figsize": (10.0, 10.0)})
+
+# %
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# Perform sensitivity analysis
+# ----------------------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 30
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+# Compute absolute error (the LRE cannot be computed,
+# because S can be zero)
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/aa932f5940d0a0978383c5019f1a7018/plot_gaussian_sum.ipynb b/otbenchmark/master/_downloads/aa932f5940d0a0978383c5019f1a7018/plot_gaussian_sum.ipynb
new file mode 100644
index 00000000000..09c06722cd3
--- /dev/null
+++ b/otbenchmark/master/_downloads/aa932f5940d0a0978383c5019f1a7018/plot_gaussian_sum.ipynb
@@ -0,0 +1,246 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the gaussian sum test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.GaussianSumSensitivity()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nprint(exact_first_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nprint(exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the function\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "marginal_distribution = ot.ComposedDistribution(\n    [\n        ot.KernelSmoothing().build(full_sample.getMarginal(i))\n        for i in range(1 + dimension)\n    ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\nview = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform sensitivity analysis\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/otbenchmark/master/_downloads/ac791b18603462477ad30d69d47bc91f/plot_nloscillator_sensitivity.py b/otbenchmark/master/_downloads/ac791b18603462477ad30d69d47bc91f/plot_nloscillator_sensitivity.py
new file mode 100644
index 00000000000..a9f04fd1256
--- /dev/null
+++ b/otbenchmark/master/_downloads/ac791b18603462477ad30d69d47bc91f/plot_nloscillator_sensitivity.py
@@ -0,0 +1,95 @@
+"""
+Benchmark the NLOscillator test function
+========================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.NLOscillatorSensitivity()
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+exact_first_order
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+exact_total_order
+
+# %%
+# Plot the function
+# -----------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+full_sample = ot.Sample(size, 1 + dimension)
+full_sample[:, range(dimension)] = inputDesign
+full_sample[:, dimension] = outputDesign
+full_description = list(inputDesign.getDescription())
+full_description.append(outputDesign.getDescription()[0])
+full_sample.setDescription(full_description)
+
+# %%
+marginal_distribution = ot.ComposedDistribution(
+    [
+        ot.KernelSmoothing().build(full_sample.getMarginal(i))
+        for i in range(1 + dimension)
+    ]
+)
+clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+_ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)})
+
+# %%
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# Perform sensitivity analysis
+# ----------------------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 10000
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+# Compute absolute error (the LRE cannot be computed,
+# because S can be zero)
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/af08bf3d5aa390154f02f9673b9ee5cb/plot_ishigami_sensitivity.ipynb b/otbenchmark/master/_downloads/af08bf3d5aa390154f02f9673b9ee5cb/plot_ishigami_sensitivity.ipynb
new file mode 100644
index 00000000000..d518d6d7b5c
--- /dev/null
+++ b/otbenchmark/master/_downloads/af08bf3d5aa390154f02f9673b9ee5cb/plot_ishigami_sensitivity.ipynb
@@ -0,0 +1,246 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the Ishigami test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.IshigamiSensitivity()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the function\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "marginal_distribution = ot.ComposedDistribution(\n    [\n        ot.KernelSmoothing().build(full_sample.getMarginal(i))\n        for i in range(1 + dimension)\n    ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (6.0, 6.0)})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform sensitivity analysis\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/otbenchmark/master/_downloads/be33b4907cde40d05f3bc08b57b462fc/plot_gsobol_sensitivity.py b/otbenchmark/master/_downloads/be33b4907cde40d05f3bc08b57b462fc/plot_gsobol_sensitivity.py
new file mode 100644
index 00000000000..a2fccfcfe79
--- /dev/null
+++ b/otbenchmark/master/_downloads/be33b4907cde40d05f3bc08b57b462fc/plot_gsobol_sensitivity.py
@@ -0,0 +1,103 @@
+"""
+Benchmark the G-Sobol test function
+===================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.GSobolSensitivity()
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+exact_first_order
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+print(exact_total_order)
+
+# %%
+# Plot function
+# -------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+full_sample = ot.Sample(size, 1 + dimension)
+full_sample[:, range(dimension)] = inputDesign
+full_sample[:, dimension] = outputDesign
+full_description = list(inputDesign.getDescription())
+full_description.append(outputDesign.getDescription()[0])
+full_sample.setDescription(full_description)
+
+# %%
+marginal_distribution = ot.ComposedDistribution(
+    [
+        ot.KernelSmoothing().build(full_sample.getMarginal(i))
+        for i in range(1 + dimension)
+    ]
+)
+clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+_ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 1000
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# Perform SA
+# ----------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 10000
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+# Compute absolute error (the LRE cannot be computed,
+# because S can be zero)
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/c1babdce08a3967dd83b589429d3cfb4/plot_dirichlet_sensitivity.py b/otbenchmark/master/_downloads/c1babdce08a3967dd83b589429d3cfb4/plot_dirichlet_sensitivity.py
new file mode 100644
index 00000000000..735ea0d657e
--- /dev/null
+++ b/otbenchmark/master/_downloads/c1babdce08a3967dd83b589429d3cfb4/plot_dirichlet_sensitivity.py
@@ -0,0 +1,95 @@
+"""
+Benchmark the Dirichlet test function
+=====================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.DirichletSensitivity()
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+exact_first_order
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+exact_total_order
+
+# %%
+# Plot the function
+# -----------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+full_sample = ot.Sample(size, 1 + dimension)
+full_sample[:, range(dimension)] = inputDesign
+full_sample[:, dimension] = outputDesign
+full_description = list(inputDesign.getDescription())
+full_description.append(outputDesign.getDescription()[0])
+full_sample.setDescription(full_description)
+
+# %%
+marginal_distribution = ot.ComposedDistribution(
+    [
+        ot.KernelSmoothing().build(full_sample.getMarginal(i))
+        for i in range(1 + dimension)
+    ]
+)
+clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+_ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+# %%
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# Perform sensitivity analysis
+# ----------------------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 10000
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+# Compute absolute error (the LRE cannot be computed,
+# because S can be zero)
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/d005867b51360ee9c2c4914f27adb11b/plot_morris_sensitivity.ipynb b/otbenchmark/master/_downloads/d005867b51360ee9c2c4914f27adb11b/plot_morris_sensitivity.ipynb
new file mode 100644
index 00000000000..018ca455715
--- /dev/null
+++ b/otbenchmark/master/_downloads/d005867b51360ee9c2c4914f27adb11b/plot_morris_sensitivity.ipynb
@@ -0,0 +1,213 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the Morris test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.MorrisSensitivity()\nprint(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the function\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nnbcolumns = 4\nnbrows = int(dimension / nbcolumns)\ngrid = ot.GridLayout(nbrows, nbcolumns)\ninputDescription = distribution.getDescription()\noutputDescription = model.getOutputDescription()[0]\nindex = 0\nfor i in range(nbrows):\n    for j in range(nbcolumns):\n        graph = ot.Graph(\n            \"n=%d\" % (size), inputDescription[index], outputDescription, True, \"\"\n        )\n        sample = ot.Sample(size, 2)\n        sample[:, 0] = inputDesign[:, index]\n        sample[:, 1] = outputDesign[:, 0]\n        cloud = ot.Cloud(sample)\n        graph.add(cloud)\n        grid.setGraph(i, j, graph)\n        index += 1\n_ = otv.View(grid, figure_kw={\"figsize\": (10.0, 10.0)})\n\n# %\noutput_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform sensitivity analysis\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 30\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/otbenchmark/master/_downloads/d0644c1b59c97e301533d2cc27d6f994/plot_borehole_sensitivity.ipynb b/otbenchmark/master/_downloads/d0644c1b59c97e301533d2cc27d6f994/plot_borehole_sensitivity.ipynb
new file mode 100644
index 00000000000..7067cfe78af
--- /dev/null
+++ b/otbenchmark/master/_downloads/d0644c1b59c97e301533d2cc27d6f994/plot_borehole_sensitivity.ipynb
@@ -0,0 +1,235 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the Borehole test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.BoreholeSensitivity()\nprint(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nexact_first_order"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nexact_total_order"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the function\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nfull_sample = ot.Sample(size, 1 + dimension)\nfull_sample[:, range(dimension)] = inputDesign\nfull_sample[:, dimension] = outputDesign\nfull_description = list(inputDesign.getDescription())\nfull_description.append(outputDesign.getDescription()[0])\nfull_sample.setDescription(full_description)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "marginal_distribution = ot.ComposedDistribution(\n    [\n        ot.KernelSmoothing().build(full_sample.getMarginal(i))\n        for i in range(1 + dimension)\n    ]\n)\nclouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)\n_ = otv.View(clouds, figure_kw={\"figsize\": (10.0, 10.0)})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform sensitivity analysis\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 10000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/otbenchmark/master/_downloads/d54ce2324b0b413ac3dffc378d3ed7a8/plot_methodFactory.zip b/otbenchmark/master/_downloads/d54ce2324b0b413ac3dffc378d3ed7a8/plot_methodFactory.zip
index 23753db6406..3b6ae6531c8 100644
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diff --git a/otbenchmark/master/_downloads/d9dfc16b3d96174b2c9ade5a59a2239c/plot_ConditionalDistribution_Demo.zip b/otbenchmark/master/_downloads/d9dfc16b3d96174b2c9ade5a59a2239c/plot_ConditionalDistribution_Demo.zip
index 0d33036731c..d8df74f36ca 100644
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diff --git a/otbenchmark/master/_downloads/e2b15a528a6607cd91fa376cbffc2002/plot_print_problems.zip b/otbenchmark/master/_downloads/e2b15a528a6607cd91fa376cbffc2002/plot_print_problems.zip
index e8f493d0b16..56b4de065f9 100644
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diff --git a/otbenchmark/master/_downloads/e6be8e01cd20ccf3d5e4421802d81703/plot_crosscut_distribution_3d.zip b/otbenchmark/master/_downloads/e6be8e01cd20ccf3d5e4421802d81703/plot_crosscut_distribution_3d.zip
index 110e0137925..75c6dd6d4e9 100644
Binary files a/otbenchmark/master/_downloads/e6be8e01cd20ccf3d5e4421802d81703/plot_crosscut_distribution_3d.zip and b/otbenchmark/master/_downloads/e6be8e01cd20ccf3d5e4421802d81703/plot_crosscut_distribution_3d.zip differ
diff --git a/otbenchmark/master/_downloads/e830ec759701c1a03cbee2f23827aba1/plot_borgonovo_sensitivity.py b/otbenchmark/master/_downloads/e830ec759701c1a03cbee2f23827aba1/plot_borgonovo_sensitivity.py
new file mode 100644
index 00000000000..bc822331e17
--- /dev/null
+++ b/otbenchmark/master/_downloads/e830ec759701c1a03cbee2f23827aba1/plot_borgonovo_sensitivity.py
@@ -0,0 +1,95 @@
+"""
+Benchmark the Borgonovo test function
+=====================================
+"""
+
+# %%
+import openturns as ot
+import otbenchmark as otb
+import openturns.viewer as otv
+
+# %%
+problem = otb.BorgonovoSensitivity()
+print(problem)
+
+# %%
+distribution = problem.getInputDistribution()
+model = problem.getFunction()
+
+# %%
+# Exact first and total order
+exact_first_order = problem.getFirstOrderIndices()
+exact_first_order
+
+# %%
+exact_total_order = problem.getTotalOrderIndices()
+exact_total_order
+
+# %%
+# Plot the function
+# -----------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 200
+inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+dimension = distribution.getDimension()
+full_sample = ot.Sample(size, 1 + dimension)
+full_sample[:, range(dimension)] = inputDesign
+full_sample[:, dimension] = outputDesign
+full_description = list(inputDesign.getDescription())
+full_description.append(outputDesign.getDescription()[0])
+full_sample.setDescription(full_description)
+
+# %%
+marginal_distribution = ot.ComposedDistribution(
+    [
+        ot.KernelSmoothing().build(full_sample.getMarginal(i))
+        for i in range(1 + dimension)
+    ]
+)
+clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+_ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+# %%
+output_distribution = ot.KernelSmoothing().build(outputDesign)
+_ = otv.View(output_distribution.drawPDF())
+
+# %%
+# Perform sensitivity analysis
+# ----------------------------
+
+# %%
+# Create X/Y data
+ot.RandomGenerator.SetSeed(0)
+size = 10000
+inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+outputDesign = model(inputDesign)
+
+# %%
+# Compute first order indices using the Saltelli estimator
+sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+# %%
+# Compare with exact results
+print("Sample size : ", size)
+# First order
+# Compute absolute error (the LRE cannot be computed,
+# because S can be zero)
+print("Computed first order = ", computed_first_order)
+print("Exact first order = ", exact_first_order)
+# Total order
+print("Computed total order = ", computed_total_order)
+print("Exact total order = ", exact_total_order)
+
+# %%
+_ = otv.View(sensitivityAnalysis.draw())
+
+# %%
+otv.View.ShowAll()
diff --git a/otbenchmark/master/_downloads/e9685936f0e9649b1b8ff6dcb9f0fc2b/plot_oakleyohagan_sensitivity.zip b/otbenchmark/master/_downloads/e9685936f0e9649b1b8ff6dcb9f0fc2b/plot_oakleyohagan_sensitivity.zip
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diff --git a/otbenchmark/master/_downloads/e9791c5b042243aaaed5b1d350b1cf7d/plot_oakleyohagan_sensitivity.ipynb b/otbenchmark/master/_downloads/e9791c5b042243aaaed5b1d350b1cf7d/plot_oakleyohagan_sensitivity.ipynb
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@@ -0,0 +1,217 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmark the Oakley-O'Hagan test function\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import openturns as ot\nimport otbenchmark as otb\nimport openturns.viewer as otv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "problem = otb.OakleyOHaganSensitivity()\nprint(problem)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "distribution = problem.getInputDistribution()\nmodel = problem.getFunction()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Exact first and total order\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_first_order = problem.getFirstOrderIndices()\nprint(exact_first_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exact_total_order = problem.getTotalOrderIndices()\nprint(exact_total_order)\n\n\n# ## Plot the function"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 200\ninputDesign = ot.MonteCarloExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dimension = distribution.getDimension()\nnbcolumns = 5\nnbrows = int(dimension / nbcolumns)\ngrid = ot.GridLayout(nbrows, nbcolumns)\ninputDescription = distribution.getDescription()\noutputDescription = model.getOutputDescription()[0]\nindex = 0\nfor i in range(nbrows):\n    for j in range(nbcolumns):\n        graph = ot.Graph(\n            \"n=%d\" % (size), inputDescription[index], outputDescription, True, \"\"\n        )\n        sample = ot.Sample(size, 2)\n        sample[:, 0] = inputDesign[:, index]\n        sample[:, 1] = outputDesign[:, 0]\n        cloud = ot.Cloud(sample)\n        graph.add(cloud)\n        grid.setGraph(i, j, graph)\n        index += 1\n_ = otv.View(grid, figure_kw={\"figsize\": (10.0, 10.0)})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "output_distribution = ot.KernelSmoothing().build(outputDesign)\n_ = otv.View(output_distribution.drawPDF())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform sensitivity analysis\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create X/Y data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ot.RandomGenerator.SetSeed(0)\nsize = 1000\ninputDesign = ot.SobolIndicesExperiment(distribution, size).generate()\noutputDesign = model(inputDesign)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compute first order indices using the Saltelli estimator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)\ncomputed_first_order = sensitivityAnalysis.getFirstOrderIndices()\ncomputed_total_order = sensitivityAnalysis.getTotalOrderIndices()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Compare with exact results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Sample size : \", size)\n# First order\n# Compute absolute error (the LRE cannot be computed,\n# because S can be zero)\nprint(\"Computed first order = \", computed_first_order)\nprint(\"Exact first order = \", exact_first_order)\n# Total order\nprint(\"Computed total order = \", computed_total_order)\nprint(\"Exact total order = \", exact_total_order)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_ = otv.View(sensitivityAnalysis.draw())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "otv.View.ShowAll()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
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diff --git a/otbenchmark/master/_sources/auto_examples/index.rst b/otbenchmark/master/_sources/auto_examples/index.rst
index a5c5db2dbec..a6dc8593650 100644
--- a/otbenchmark/master/_sources/auto_examples/index.rst
+++ b/otbenchmark/master/_sources/auto_examples/index.rst
@@ -157,6 +157,193 @@ Sensitivity examples
     </div>
 
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Flooding test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_flood_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_flood_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Flooding test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Borgonovo test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_borgonovo_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Borgonovo test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Ishigami test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_ishigami_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Ishigami test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Dirichlet test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_dirichlet_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Dirichlet test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Borehole test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_borehole_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Borehole test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the NLOscillator test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_nloscillator_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the NLOscillator test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the gaussian sum test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gaussian_sum_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_sum.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the gaussian sum test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the gaussian product test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gaussian_product_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the gaussian product test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Morris test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_morris_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_morris_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Morris test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Oakley-O&#x27;Hagan test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_oakleyohagan_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Oakley-O'Hagan test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the G-Sobol test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gsobol_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the G-Sobol test function</div>
+    </div>
+
+
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="Convergence of estimators on Ishigami">
@@ -174,6 +361,23 @@ Sensitivity examples
     </div>
 
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark sensitivity analysis methods">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_benchmark_sensitivity_methods_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark sensitivity analysis methods</div>
+    </div>
+
+
 .. thumbnail-parent-div-close
 
 .. raw:: html
diff --git a/otbenchmark/master/_sources/auto_examples/plot_ConditionalDistribution_Demo.rst b/otbenchmark/master/_sources/auto_examples/plot_ConditionalDistribution_Demo.rst
index e476fa9d04f..b2b4ef117d6 100644
--- a/otbenchmark/master/_sources/auto_examples/plot_ConditionalDistribution_Demo.rst
+++ b/otbenchmark/master/_sources/auto_examples/plot_ConditionalDistribution_Demo.rst
@@ -242,7 +242,7 @@ Create a Punk distribution
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.619 seconds)
+   **Total running time of the script:** (0 minutes 2.577 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_ConditionalDistribution_Demo.py:
diff --git a/otbenchmark/master/_sources/auto_examples/plot_CrossCutFunction_Demo.rst b/otbenchmark/master/_sources/auto_examples/plot_CrossCutFunction_Demo.rst
index 9f5655cf089..a68a90524c7 100644
--- a/otbenchmark/master/_sources/auto_examples/plot_CrossCutFunction_Demo.rst
+++ b/otbenchmark/master/_sources/auto_examples/plot_CrossCutFunction_Demo.rst
@@ -213,7 +213,7 @@ Let :math:`\bar{x}\in\mathbb{R}^3` be the reference point.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.738 seconds)
+   **Total running time of the script:** (0 minutes 1.229 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_CrossCutFunction_Demo.py:
diff --git a/otbenchmark/master/_sources/auto_examples/plot_convergence_reliability_mc.rst b/otbenchmark/master/_sources/auto_examples/plot_convergence_reliability_mc.rst
index b5c111bf60e..15d485a8174 100644
--- a/otbenchmark/master/_sources/auto_examples/plot_convergence_reliability_mc.rst
+++ b/otbenchmark/master/_sources/auto_examples/plot_convergence_reliability_mc.rst
@@ -122,7 +122,7 @@ where :math:`n` is the sample size.
 
  .. code-block:: none
 
-    Number of function calls = 1842 , Pf = 0.0765 , 95.0 % confidence interval :[0.0616,0.0915] 
+    Number of function calls = 1978 , Pf = 0.0713 , 95.0 % confidence interval :[0.0573,0.0852] 
 
 
 
@@ -186,153 +186,153 @@ where :math:`n` is the sample size.
     Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
     Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
     Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978] 
-    Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978] 
+    Number of function calls = 4 , Pf = 0.5000 , 95.0 % confidence interval :[0.0100,0.9900] 
+    Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
+    Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
     Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
-    Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978] 
     Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
     Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
     Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
-    Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978] 
-    Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] 
-    Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] 
-    Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
     Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
     Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] 
+    Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
     Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] 
-    Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] 
-    Number of function calls = 8 , Pf = 0.2500 , 95.0 % confidence interval :[-0.1374,0.6374] 
+    Number of function calls = 8 , Pf = 0.3750 , 95.0 % confidence interval :[-0.0026,0.7526] 
+    Number of function calls = 8 , Pf = 0.2500 , 95.0 % confidence interval :[-0.0501,0.5501] 
+    Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
     Number of function calls = 8 , Pf = 0.2500 , 95.0 % confidence interval :[-0.1374,0.6374] 
     Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
-    Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
-    Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] 
-    Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
+    Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123] 
     Number of function calls = 16 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0781,0.3281] 
     Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] 
     Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] 
-    Number of function calls = 16 , Pf = 0.2500 , 95.0 % confidence interval :[-0.0239,0.5239] 
     Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] 
-    Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
+    Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] 
+    Number of function calls = 16 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0370,0.2870] 
+    Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] 
+    Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094] 
+    Number of function calls = 16 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0781,0.3281] 
     Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
     Number of function calls = 32 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0430,0.1055] 
-    Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0161,0.2036] 
-    Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664] 
+    Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0321,0.2196] 
     Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664] 
+    Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0321,0.2196] 
+    Number of function calls = 32 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000] 
+    Number of function calls = 32 , Pf = 0.1875 , 95.0 % confidence interval :[0.0390,0.3360] 
+    Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0321,0.2196] 
     Number of function calls = 32 , Pf = 0.1562 , 95.0 % confidence interval :[-0.0025,0.3150] 
-    Number of function calls = 32 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0430,0.1055] 
-    Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664] 
-    Number of function calls = 32 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0430,0.1055] 
-    Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0214,0.1464] 
     Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664] 
-    Number of function calls = 64 , Pf = 0.1406 , 95.0 % confidence interval :[0.0335,0.2477] 
-    Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0102,0.1773] 
-    Number of function calls = 64 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0109,0.1359] 
-    Number of function calls = 64 , Pf = 0.1094 , 95.0 % confidence interval :[0.0138,0.2049] 
-    Number of function calls = 64 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0212,0.0837] 
-    Number of function calls = 64 , Pf = 0.1719 , 95.0 % confidence interval :[0.0675,0.2762] 
+    Number of function calls = 32 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0186,0.2686] 
+    Number of function calls = 64 , Pf = 0.0781 , 95.0 % confidence interval :[-0.0035,0.1598] 
+    Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0048,0.1827] 
     Number of function calls = 64 , Pf = 0.0469 , 95.0 % confidence interval :[-0.0171,0.1108] 
+    Number of function calls = 64 , Pf = 0.1562 , 95.0 % confidence interval :[0.0440,0.2685] 
     Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0048,0.1827] 
-    Number of function calls = 64 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0109,0.1359] 
-    Number of function calls = 64 , Pf = 0.0781 , 95.0 % confidence interval :[-0.0035,0.1598] 
-    Number of function calls = 128 , Pf = 0.0937 , 95.0 % confidence interval :[0.0308,0.1567] 
-    Number of function calls = 128 , Pf = 0.0781 , 95.0 % confidence interval :[0.0204,0.1359] 
-    Number of function calls = 128 , Pf = 0.0781 , 95.0 % confidence interval :[0.0204,0.1359] 
-    Number of function calls = 128 , Pf = 0.1016 , 95.0 % confidence interval :[0.0363,0.1669] 
-    Number of function calls = 128 , Pf = 0.1172 , 95.0 % confidence interval :[0.0475,0.1869] 
-    Number of function calls = 128 , Pf = 0.0703 , 95.0 % confidence interval :[0.0154,0.1252] 
+    Number of function calls = 64 , Pf = 0.0469 , 95.0 % confidence interval :[-0.0171,0.1108] 
+    Number of function calls = 64 , Pf = 0.1719 , 95.0 % confidence interval :[0.0589,0.2849] 
+    Number of function calls = 64 , Pf = 0.0781 , 95.0 % confidence interval :[0.0024,0.1538] 
+    Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0102,0.1773] 
+    Number of function calls = 64 , Pf = 0.0469 , 95.0 % confidence interval :[-0.0171,0.1108] 
+    Number of function calls = 128 , Pf = 0.1094 , 95.0 % confidence interval :[0.0418,0.1769] 
+    Number of function calls = 128 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0059,0.0684] 
+    Number of function calls = 128 , Pf = 0.1250 , 95.0 % confidence interval :[0.0548,0.1952] 
+    Number of function calls = 128 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0059,0.0684] 
+    Number of function calls = 128 , Pf = 0.0469 , 95.0 % confidence interval :[0.0017,0.0921] 
     Number of function calls = 128 , Pf = 0.0391 , 95.0 % confidence interval :[-0.0023,0.0804] 
-    Number of function calls = 128 , Pf = 0.0859 , 95.0 % confidence interval :[0.0255,0.1463] 
-    Number of function calls = 128 , Pf = 0.0547 , 95.0 % confidence interval :[0.0060,0.1034] 
-    Number of function calls = 128 , Pf = 0.0938 , 95.0 % confidence interval :[0.0327,0.1548] 
-    Number of function calls = 256 , Pf = 0.0781 , 95.0 % confidence interval :[0.0380,0.1182] 
+    Number of function calls = 128 , Pf = 0.0859 , 95.0 % confidence interval :[0.0275,0.1444] 
+    Number of function calls = 128 , Pf = 0.1172 , 95.0 % confidence interval :[0.0492,0.1852] 
+    Number of function calls = 128 , Pf = 0.0703 , 95.0 % confidence interval :[0.0154,0.1252] 
+    Number of function calls = 128 , Pf = 0.1016 , 95.0 % confidence interval :[0.0381,0.1650] 
+    Number of function calls = 256 , Pf = 0.0820 , 95.0 % confidence interval :[0.0417,0.1224] 
     Number of function calls = 256 , Pf = 0.0859 , 95.0 % confidence interval :[0.0432,0.1286] 
-    Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0286,0.1042] 
-    Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0286,0.1042] 
-    Number of function calls = 256 , Pf = 0.0742 , 95.0 % confidence interval :[0.0344,0.1141] 
-    Number of function calls = 256 , Pf = 0.0742 , 95.0 % confidence interval :[0.0351,0.1133] 
-    Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0286,0.1042] 
-    Number of function calls = 256 , Pf = 0.0586 , 95.0 % confidence interval :[0.0238,0.0934] 
-    Number of function calls = 256 , Pf = 0.1016 , 95.0 % confidence interval :[0.0554,0.1477] 
-    Number of function calls = 256 , Pf = 0.0977 , 95.0 % confidence interval :[0.0536,0.1417] 
-    Number of function calls = 512 , Pf = 0.0508 , 95.0 % confidence interval :[0.0276,0.0740] 
+    Number of function calls = 256 , Pf = 0.0781 , 95.0 % confidence interval :[0.0373,0.1190] 
+    Number of function calls = 256 , Pf = 0.0625 , 95.0 % confidence interval :[0.0266,0.0984] 
+    Number of function calls = 256 , Pf = 0.0781 , 95.0 % confidence interval :[0.0380,0.1182] 
+    Number of function calls = 256 , Pf = 0.0352 , 95.0 % confidence interval :[0.0074,0.0630] 
+    Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0302,0.1026] 
+    Number of function calls = 256 , Pf = 0.0547 , 95.0 % confidence interval :[0.0202,0.0891] 
+    Number of function calls = 256 , Pf = 0.0625 , 95.0 % confidence interval :[0.0258,0.0992] 
+    Number of function calls = 256 , Pf = 0.0703 , 95.0 % confidence interval :[0.0315,0.1092] 
     Number of function calls = 512 , Pf = 0.0723 , 95.0 % confidence interval :[0.0450,0.0996] 
-    Number of function calls = 512 , Pf = 0.0762 , 95.0 % confidence interval :[0.0482,0.1042] 
-    Number of function calls = 512 , Pf = 0.0645 , 95.0 % confidence interval :[0.0390,0.0900] 
-    Number of function calls = 512 , Pf = 0.0645 , 95.0 % confidence interval :[0.0381,0.0908] 
-    Number of function calls = 512 , Pf = 0.0762 , 95.0 % confidence interval :[0.0490,0.1034] 
-    Number of function calls = 512 , Pf = 0.0586 , 95.0 % confidence interval :[0.0334,0.0838] 
-    Number of function calls = 512 , Pf = 0.1055 , 95.0 % confidence interval :[0.0729,0.1380] 
-    Number of function calls = 512 , Pf = 0.0781 , 95.0 % confidence interval :[0.0498,0.1065] 
-    Number of function calls = 512 , Pf = 0.0840 , 95.0 % confidence interval :[0.0546,0.1134] 
-    Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0639,0.1060] 
+    Number of function calls = 512 , Pf = 0.0605 , 95.0 % confidence interval :[0.0358,0.0852] 
+    Number of function calls = 512 , Pf = 0.0996 , 95.0 % confidence interval :[0.0686,0.1306] 
+    Number of function calls = 512 , Pf = 0.0938 , 95.0 % confidence interval :[0.0628,0.1247] 
+    Number of function calls = 512 , Pf = 0.0742 , 95.0 % confidence interval :[0.0471,0.1013] 
+    Number of function calls = 512 , Pf = 0.0723 , 95.0 % confidence interval :[0.0450,0.0996] 
+    Number of function calls = 512 , Pf = 0.0684 , 95.0 % confidence interval :[0.0415,0.0952] 
+    Number of function calls = 512 , Pf = 0.0859 , 95.0 % confidence interval :[0.0565,0.1154] 
+    Number of function calls = 512 , Pf = 0.0938 , 95.0 % confidence interval :[0.0628,0.1247] 
+    Number of function calls = 512 , Pf = 0.0566 , 95.0 % confidence interval :[0.0322,0.0811] 
+    Number of function calls = 1024 , Pf = 0.0713 , 95.0 % confidence interval :[0.0519,0.0906] 
+    Number of function calls = 1024 , Pf = 0.0791 , 95.0 % confidence interval :[0.0588,0.0994] 
+    Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0638,0.1061] 
+    Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0640,0.1059] 
+    Number of function calls = 1024 , Pf = 0.0830 , 95.0 % confidence interval :[0.0625,0.1035] 
+    Number of function calls = 1024 , Pf = 0.0869 , 95.0 % confidence interval :[0.0658,0.1080] 
+    Number of function calls = 1024 , Pf = 0.0869 , 95.0 % confidence interval :[0.0657,0.1081] 
+    Number of function calls = 1024 , Pf = 0.0771 , 95.0 % confidence interval :[0.0570,0.0973] 
+    Number of function calls = 1024 , Pf = 0.0830 , 95.0 % confidence interval :[0.0622,0.1038] 
     Number of function calls = 1024 , Pf = 0.0742 , 95.0 % confidence interval :[0.0546,0.0939] 
-    Number of function calls = 1024 , Pf = 0.0889 , 95.0 % confidence interval :[0.0675,0.1102] 
-    Number of function calls = 1024 , Pf = 0.0674 , 95.0 % confidence interval :[0.0485,0.0862] 
-    Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0642,0.1058] 
-    Number of function calls = 1024 , Pf = 0.0635 , 95.0 % confidence interval :[0.0456,0.0814] 
-    Number of function calls = 1024 , Pf = 0.0781 , 95.0 % confidence interval :[0.0580,0.0983] 
-    Number of function calls = 1024 , Pf = 0.0967 , 95.0 % confidence interval :[0.0744,0.1189] 
-    Number of function calls = 1024 , Pf = 0.0820 , 95.0 % confidence interval :[0.0615,0.1026] 
-    Number of function calls = 1024 , Pf = 0.0752 , 95.0 % confidence interval :[0.0553,0.0951] 
-    Number of function calls = 2048 , Pf = 0.0786 , 95.0 % confidence interval :[0.0644,0.0929] 
-    Number of function calls = 2048 , Pf = 0.0776 , 95.0 % confidence interval :[0.0636,0.0917] 
-    Number of function calls = 2048 , Pf = 0.0840 , 95.0 % confidence interval :[0.0692,0.0988] 
-    Number of function calls = 2048 , Pf = 0.0664 , 95.0 % confidence interval :[0.0531,0.0797] 
+    Number of function calls = 2048 , Pf = 0.0840 , 95.0 % confidence interval :[0.0693,0.0987] 
+    Number of function calls = 2048 , Pf = 0.0864 , 95.0 % confidence interval :[0.0716,0.1013] 
+    Number of function calls = 2048 , Pf = 0.0894 , 95.0 % confidence interval :[0.0743,0.1044] 
+    Number of function calls = 2048 , Pf = 0.0864 , 95.0 % confidence interval :[0.0715,0.1014] 
+    Number of function calls = 2048 , Pf = 0.0757 , 95.0 % confidence interval :[0.0618,0.0896] 
+    Number of function calls = 2048 , Pf = 0.0781 , 95.0 % confidence interval :[0.0638,0.0925] 
     Number of function calls = 2048 , Pf = 0.0786 , 95.0 % confidence interval :[0.0643,0.0930] 
-    Number of function calls = 2048 , Pf = 0.0742 , 95.0 % confidence interval :[0.0605,0.0880] 
-    Number of function calls = 2048 , Pf = 0.0791 , 95.0 % confidence interval :[0.0647,0.0935] 
-    Number of function calls = 2048 , Pf = 0.0815 , 95.0 % confidence interval :[0.0670,0.0961] 
-    Number of function calls = 2048 , Pf = 0.0767 , 95.0 % confidence interval :[0.0626,0.0907] 
-    Number of function calls = 2048 , Pf = 0.0801 , 95.0 % confidence interval :[0.0658,0.0944] 
-    Number of function calls = 4096 , Pf = 0.0820 , 95.0 % confidence interval :[0.0717,0.0923] 
-    Number of function calls = 4096 , Pf = 0.0789 , 95.0 % confidence interval :[0.0688,0.0889] 
-    Number of function calls = 4096 , Pf = 0.0754 , 95.0 % confidence interval :[0.0656,0.0853] 
-    Number of function calls = 4096 , Pf = 0.0806 , 95.0 % confidence interval :[0.0703,0.0908] 
-    Number of function calls = 4096 , Pf = 0.0833 , 95.0 % confidence interval :[0.0728,0.0937] 
-    Number of function calls = 4096 , Pf = 0.0786 , 95.0 % confidence interval :[0.0686,0.0887] 
-    Number of function calls = 4096 , Pf = 0.0720 , 95.0 % confidence interval :[0.0623,0.0817] 
-    Number of function calls = 4096 , Pf = 0.0723 , 95.0 % confidence interval :[0.0625,0.0820] 
-    Number of function calls = 4096 , Pf = 0.0754 , 95.0 % confidence interval :[0.0656,0.0853] 
-    Number of function calls = 4096 , Pf = 0.0732 , 95.0 % confidence interval :[0.0635,0.0830] 
-    Number of function calls = 8192 , Pf = 0.0786 , 95.0 % confidence interval :[0.0715,0.0858] 
+    Number of function calls = 2048 , Pf = 0.0747 , 95.0 % confidence interval :[0.0609,0.0885] 
+    Number of function calls = 2048 , Pf = 0.0674 , 95.0 % confidence interval :[0.0542,0.0806] 
+    Number of function calls = 2048 , Pf = 0.0796 , 95.0 % confidence interval :[0.0653,0.0938] 
+    Number of function calls = 4096 , Pf = 0.0735 , 95.0 % confidence interval :[0.0637,0.0833] 
+    Number of function calls = 4096 , Pf = 0.0774 , 95.0 % confidence interval :[0.0674,0.0874] 
+    Number of function calls = 4096 , Pf = 0.0825 , 95.0 % confidence interval :[0.0722,0.0929] 
+    Number of function calls = 4096 , Pf = 0.0764 , 95.0 % confidence interval :[0.0665,0.0863] 
+    Number of function calls = 4096 , Pf = 0.0815 , 95.0 % confidence interval :[0.0713,0.0918] 
+    Number of function calls = 4096 , Pf = 0.0791 , 95.0 % confidence interval :[0.0689,0.0893] 
+    Number of function calls = 4096 , Pf = 0.0784 , 95.0 % confidence interval :[0.0682,0.0885] 
+    Number of function calls = 4096 , Pf = 0.0740 , 95.0 % confidence interval :[0.0641,0.0838] 
+    Number of function calls = 4096 , Pf = 0.0747 , 95.0 % confidence interval :[0.0649,0.0845] 
+    Number of function calls = 4096 , Pf = 0.0815 , 95.0 % confidence interval :[0.0712,0.0918] 
+    Number of function calls = 8192 , Pf = 0.0782 , 95.0 % confidence interval :[0.0711,0.0854] 
+    Number of function calls = 8192 , Pf = 0.0781 , 95.0 % confidence interval :[0.0710,0.0852] 
+    Number of function calls = 8192 , Pf = 0.0770 , 95.0 % confidence interval :[0.0700,0.0841] 
+    Number of function calls = 8192 , Pf = 0.0768 , 95.0 % confidence interval :[0.0697,0.0838] 
+    Number of function calls = 8192 , Pf = 0.0745 , 95.0 % confidence interval :[0.0675,0.0815] 
+    Number of function calls = 8192 , Pf = 0.0780 , 95.0 % confidence interval :[0.0709,0.0851] 
+    Number of function calls = 8192 , Pf = 0.0797 , 95.0 % confidence interval :[0.0725,0.0869] 
     Number of function calls = 8192 , Pf = 0.0797 , 95.0 % confidence interval :[0.0725,0.0869] 
-    Number of function calls = 8192 , Pf = 0.0785 , 95.0 % confidence interval :[0.0713,0.0856] 
-    Number of function calls = 8192 , Pf = 0.0809 , 95.0 % confidence interval :[0.0737,0.0882] 
-    Number of function calls = 8192 , Pf = 0.0785 , 95.0 % confidence interval :[0.0714,0.0856] 
-    Number of function calls = 8192 , Pf = 0.0764 , 95.0 % confidence interval :[0.0694,0.0834] 
-    Number of function calls = 8192 , Pf = 0.0833 , 95.0 % confidence interval :[0.0759,0.0906] 
-    Number of function calls = 8192 , Pf = 0.0803 , 95.0 % confidence interval :[0.0731,0.0875] 
-    Number of function calls = 8192 , Pf = 0.0789 , 95.0 % confidence interval :[0.0717,0.0860] 
-    Number of function calls = 8192 , Pf = 0.0859 , 95.0 % confidence interval :[0.0785,0.0934] 
-    Number of function calls = 16384 , Pf = 0.0776 , 95.0 % confidence interval :[0.0726,0.0827] 
-    Number of function calls = 16384 , Pf = 0.0801 , 95.0 % confidence interval :[0.0750,0.0852] 
-    Number of function calls = 16384 , Pf = 0.0792 , 95.0 % confidence interval :[0.0741,0.0842] 
-    Number of function calls = 16384 , Pf = 0.0768 , 95.0 % confidence interval :[0.0718,0.0818] 
-    Number of function calls = 16384 , Pf = 0.0770 , 95.0 % confidence interval :[0.0720,0.0820] 
-    Number of function calls = 16384 , Pf = 0.0778 , 95.0 % confidence interval :[0.0728,0.0829] 
-    Number of function calls = 16384 , Pf = 0.0804 , 95.0 % confidence interval :[0.0753,0.0855] 
-    Number of function calls = 16384 , Pf = 0.0817 , 95.0 % confidence interval :[0.0765,0.0868] 
-    Number of function calls = 16384 , Pf = 0.0779 , 95.0 % confidence interval :[0.0728,0.0829] 
-    Number of function calls = 16384 , Pf = 0.0802 , 95.0 % confidence interval :[0.0751,0.0853] 
-    Number of function calls = 32768 , Pf = 0.0785 , 95.0 % confidence interval :[0.0749,0.0821] 
-    Number of function calls = 32768 , Pf = 0.0782 , 95.0 % confidence interval :[0.0746,0.0818] 
-    Number of function calls = 32768 , Pf = 0.0803 , 95.0 % confidence interval :[0.0767,0.0839] 
-    Number of function calls = 32768 , Pf = 0.0782 , 95.0 % confidence interval :[0.0747,0.0818] 
+    Number of function calls = 8192 , Pf = 0.0760 , 95.0 % confidence interval :[0.0690,0.0831] 
+    Number of function calls = 8192 , Pf = 0.0818 , 95.0 % confidence interval :[0.0745,0.0891] 
+    Number of function calls = 16384 , Pf = 0.0825 , 95.0 % confidence interval :[0.0773,0.0876] 
+    Number of function calls = 16384 , Pf = 0.0812 , 95.0 % confidence interval :[0.0761,0.0863] 
+    Number of function calls = 16384 , Pf = 0.0760 , 95.0 % confidence interval :[0.0710,0.0810] 
+    Number of function calls = 16384 , Pf = 0.0790 , 95.0 % confidence interval :[0.0740,0.0841] 
+    Number of function calls = 16384 , Pf = 0.0793 , 95.0 % confidence interval :[0.0743,0.0844] 
+    Number of function calls = 16384 , Pf = 0.0806 , 95.0 % confidence interval :[0.0755,0.0857] 
+    Number of function calls = 16384 , Pf = 0.0785 , 95.0 % confidence interval :[0.0735,0.0835] 
+    Number of function calls = 16384 , Pf = 0.0779 , 95.0 % confidence interval :[0.0729,0.0830] 
+    Number of function calls = 16384 , Pf = 0.0786 , 95.0 % confidence interval :[0.0736,0.0837] 
+    Number of function calls = 16384 , Pf = 0.0750 , 95.0 % confidence interval :[0.0701,0.0799] 
+    Number of function calls = 32768 , Pf = 0.0772 , 95.0 % confidence interval :[0.0737,0.0808] 
+    Number of function calls = 32768 , Pf = 0.0766 , 95.0 % confidence interval :[0.0731,0.0801] 
+    Number of function calls = 32768 , Pf = 0.0796 , 95.0 % confidence interval :[0.0760,0.0832] 
     Number of function calls = 32768 , Pf = 0.0802 , 95.0 % confidence interval :[0.0766,0.0838] 
-    Number of function calls = 32768 , Pf = 0.0775 , 95.0 % confidence interval :[0.0740,0.0811] 
-    Number of function calls = 32768 , Pf = 0.0794 , 95.0 % confidence interval :[0.0758,0.0830] 
-    Number of function calls = 32768 , Pf = 0.0794 , 95.0 % confidence interval :[0.0758,0.0830] 
-    Number of function calls = 32768 , Pf = 0.0799 , 95.0 % confidence interval :[0.0763,0.0835] 
-    Number of function calls = 32768 , Pf = 0.0789 , 95.0 % confidence interval :[0.0753,0.0825] 
-    Number of function calls = 65536 , Pf = 0.0781 , 95.0 % confidence interval :[0.0756,0.0806] 
-    Number of function calls = 65536 , Pf = 0.0777 , 95.0 % confidence interval :[0.0752,0.0802] 
-    Number of function calls = 65536 , Pf = 0.0804 , 95.0 % confidence interval :[0.0779,0.0830] 
-    Number of function calls = 65536 , Pf = 0.0778 , 95.0 % confidence interval :[0.0753,0.0803] 
+    Number of function calls = 32768 , Pf = 0.0796 , 95.0 % confidence interval :[0.0760,0.0831] 
+    Number of function calls = 32768 , Pf = 0.0772 , 95.0 % confidence interval :[0.0737,0.0807] 
+    Number of function calls = 32768 , Pf = 0.0786 , 95.0 % confidence interval :[0.0750,0.0822] 
+    Number of function calls = 32768 , Pf = 0.0778 , 95.0 % confidence interval :[0.0743,0.0814] 
+    Number of function calls = 32768 , Pf = 0.0810 , 95.0 % confidence interval :[0.0774,0.0846] 
+    Number of function calls = 32768 , Pf = 0.0808 , 95.0 % confidence interval :[0.0772,0.0844] 
+    Number of function calls = 65536 , Pf = 0.0790 , 95.0 % confidence interval :[0.0765,0.0815] 
+    Number of function calls = 65536 , Pf = 0.0774 , 95.0 % confidence interval :[0.0749,0.0799] 
     Number of function calls = 65536 , Pf = 0.0786 , 95.0 % confidence interval :[0.0761,0.0812] 
-    Number of function calls = 65536 , Pf = 0.0779 , 95.0 % confidence interval :[0.0754,0.0804] 
-    Number of function calls = 65536 , Pf = 0.0818 , 95.0 % confidence interval :[0.0792,0.0844] 
-    Number of function calls = 65536 , Pf = 0.0793 , 95.0 % confidence interval :[0.0768,0.0819] 
-    Number of function calls = 65536 , Pf = 0.0786 , 95.0 % confidence interval :[0.0761,0.0811] 
-    Number of function calls = 65536 , Pf = 0.0814 , 95.0 % confidence interval :[0.0788,0.0839] 
+    Number of function calls = 65536 , Pf = 0.0781 , 95.0 % confidence interval :[0.0756,0.0807] 
+    Number of function calls = 65536 , Pf = 0.0793 , 95.0 % confidence interval :[0.0767,0.0818] 
+    Number of function calls = 65536 , Pf = 0.0793 , 95.0 % confidence interval :[0.0768,0.0818] 
+    Number of function calls = 65536 , Pf = 0.0806 , 95.0 % confidence interval :[0.0781,0.0832] 
+    Number of function calls = 65536 , Pf = 0.0784 , 95.0 % confidence interval :[0.0759,0.0810] 
+    Number of function calls = 65536 , Pf = 0.0783 , 95.0 % confidence interval :[0.0758,0.0808] 
+    Number of function calls = 65536 , Pf = 0.0789 , 95.0 % confidence interval :[0.0763,0.0814] 
 
 
 
@@ -352,7 +352,7 @@ where :math:`n` is the sample size.
 
  .. code-block:: none
 
-    Elapsed = 1.75 (s)
+    Elapsed = 1.74 (s)
 
 
 
@@ -414,7 +414,7 @@ where :math:`n` is the sample size.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.174 seconds)
+   **Total running time of the script:** (0 minutes 2.177 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_convergence_reliability_mc.py:
diff --git a/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_2d.rst b/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_2d.rst
index 0fe72ae277f..7638d14386d 100644
--- a/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_2d.rst
+++ b/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_2d.rst
@@ -152,7 +152,7 @@ Plot cross-cuts of the distribution
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.997 seconds)
+   **Total running time of the script:** (0 minutes 2.066 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_crosscut_distribution_2d.py:
diff --git a/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_3d.rst b/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_3d.rst
index d8967bce4a5..358c323007a 100644
--- a/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_3d.rst
+++ b/otbenchmark/master/_sources/auto_examples/plot_crosscut_distribution_3d.rst
@@ -146,7 +146,7 @@ Print the iso-values of the distribution
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.366 seconds)
+   **Total running time of the script:** (0 minutes 7.724 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_crosscut_distribution_3d.py:
diff --git a/otbenchmark/master/_sources/auto_examples/plot_methodFactory.rst b/otbenchmark/master/_sources/auto_examples/plot_methodFactory.rst
index 440a6975aa9..0b51e0c6633 100644
--- a/otbenchmark/master/_sources/auto_examples/plot_methodFactory.rst
+++ b/otbenchmark/master/_sources/auto_examples/plot_methodFactory.rst
@@ -331,7 +331,7 @@ Create a LHS algorithm
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.439 seconds)
+   **Total running time of the script:** (0 minutes 7.465 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_methodFactory.py:
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/index.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/index.rst
index 51535d15c69..1cc1b1cd3ef 100644
--- a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/index.rst
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/index.rst
@@ -30,6 +30,193 @@ Sensitivity examples
     </div>
 
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Flooding test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_flood_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_flood_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Flooding test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Borgonovo test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_borgonovo_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Borgonovo test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Ishigami test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_ishigami_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Ishigami test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Dirichlet test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_dirichlet_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Dirichlet test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Borehole test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_borehole_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Borehole test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the NLOscillator test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_nloscillator_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the NLOscillator test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the gaussian sum test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gaussian_sum_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_sum.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the gaussian sum test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the gaussian product test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gaussian_product_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the gaussian product test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Morris test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_morris_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_morris_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Morris test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Oakley-O&#x27;Hagan test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_oakleyohagan_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the Oakley-O'Hagan test function</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark the G-Sobol test function">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_gsobol_sensitivity_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark the G-Sobol test function</div>
+    </div>
+
+
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="Convergence of estimators on Ishigami">
@@ -47,6 +234,23 @@ Sensitivity examples
     </div>
 
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Benchmark sensitivity analysis methods">
+
+.. only:: html
+
+  .. image:: /auto_examples/sensitivity_problems/images/thumb/sphx_glr_plot_benchmark_sensitivity_methods_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Benchmark sensitivity analysis methods</div>
+    </div>
+
+
 .. thumbnail-parent-div-close
 
 .. raw:: html
@@ -58,5 +262,17 @@ Sensitivity examples
    :hidden:
 
    /auto_examples/sensitivity_problems/plot_print_problems
+   /auto_examples/sensitivity_problems/plot_flood_sensitivity
+   /auto_examples/sensitivity_problems/plot_borgonovo_sensitivity
+   /auto_examples/sensitivity_problems/plot_ishigami_sensitivity
+   /auto_examples/sensitivity_problems/plot_dirichlet_sensitivity
+   /auto_examples/sensitivity_problems/plot_borehole_sensitivity
+   /auto_examples/sensitivity_problems/plot_nloscillator_sensitivity
+   /auto_examples/sensitivity_problems/plot_gaussian_sum
+   /auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity
+   /auto_examples/sensitivity_problems/plot_morris_sensitivity
+   /auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity
+   /auto_examples/sensitivity_problems/plot_gsobol_sensitivity
    /auto_examples/sensitivity_problems/plot_convergence_ishigami
+   /auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods
 
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.rst
new file mode 100644
index 00000000000..e7958717c8a
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.rst
@@ -0,0 +1,576 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py:
+
+
+Benchmark sensitivity analysis methods
+======================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-10
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 11-17
+
+When we estimate Sobol' indices, we may encounter the following warning messages:
+```
+WRN - The estimated first order Sobol index (2) is greater than its total order index ...
+WRN - The estimated total order Sobol index (2) is lesser than first order index ...
+```
+Lots of these messages are printed in the current Notebook. This is why we disable them with:
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-19
+
+.. code-block:: Python
+
+    ot.Log.Show(ot.Log.NONE)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-21
+
+Use Borgonovo problem
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-25
+
+.. code-block:: Python
+
+    problem = otb.BorgonovoSensitivity()
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 26-27
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 27-30
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    exact_total_order = problem.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 31-33
+
+Saltelli estimator with Monte-Carlo sample
+------------------------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-37
+
+.. code-block:: Python
+
+    sample_size = 10000
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 38-41
+
+.. code-block:: Python
+
+    inputDesign = ot.SobolIndicesExperiment(distribution, sample_size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 42-43
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 43-49
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(
+        inputDesign, outputDesign, sample_size
+    )
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 50-51
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 51-59
+
+.. code-block:: Python
+
+    print("Sample size : ", sample_size)
+    # First order
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  10000
+    Computed first order =  [0.18553,0.173199,0.637105]
+    Exact first order =  [0.157895,0.157895,0.631579]
+    Computed total order =  [0.203344,0.210474,0.620406]
+    Exact total order =  [0.210526,0.210526,0.631579]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-62
+
+Saltelli estimator with Quasi Monte-Carlo sample
+------------------------------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 64-66
+
+.. code-block:: Python
+
+    sample_size = 500
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-72
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    sequence = ot.SobolSequence(dimension)
+    restart = True
+    experiment = ot.LowDiscrepancyExperiment(sequence, distribution, sample_size, restart)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 73-76
+
+.. code-block:: Python
+
+    inputDesign = ot.SobolIndicesExperiment(experiment).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-78
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 78-84
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(
+        inputDesign, outputDesign, sample_size
+    )
+    first_order = sensitivityAnalysis.getFirstOrderIndices()
+    total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-86
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 86-94
+
+.. code-block:: Python
+
+    print("Sample size : ", sample_size)
+    # First order
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  500
+    Computed first order =  [0.18553,0.173199,0.637105]
+    Exact first order =  [0.157895,0.157895,0.631579]
+    Computed total order =  [0.203344,0.210474,0.620406]
+    Exact total order =  [0.210526,0.210526,0.631579]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-97
+
+Loop over the estimators
+------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 99-105
+
+.. code-block:: Python
+
+    print("Available estimators:")
+    estimators_list = otb.SensitivityBenchmarkMetaAlgorithm.GetEstimators()
+    for sobolAlgorithm in estimators_list:
+        name = sobolAlgorithm.getClassName()
+        print(" - ", name)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Available estimators:
+     -  SaltelliSensitivityAlgorithm
+     -  MartinezSensitivityAlgorithm
+     -  JansenSensitivityAlgorithm
+     -  MauntzKucherenkoSensitivityAlgorithm
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-108
+
+.. code-block:: Python
+
+    metaSAAlgorithm = otb.SensitivityBenchmarkMetaAlgorithm(problem)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 109-120
+
+.. code-block:: Python
+
+    print("Monte-Carlo sampling")
+    for sobolAlgorithm in estimators_list:
+        (
+            computed_first_order,
+            computed_total_order,
+        ) = metaSAAlgorithm.runSamplingEstimator(sample_size)
+        name = sobolAlgorithm.getClassName()
+        print(name)
+        print("    S = ", computed_first_order)
+        print("    T = ", computed_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Monte-Carlo sampling
+    SaltelliSensitivityAlgorithm
+        S =  [0.199997,0.180685,0.664071]
+        T =  [0.223721,0.228732,0.6502]
+    MartinezSensitivityAlgorithm
+        S =  [0.191903,0.173622,0.638601]
+        T =  [0.2242,0.207898,0.591462]
+    JansenSensitivityAlgorithm
+        S =  [0.0952801,0.125079,0.615803]
+        T =  [0.253686,0.237742,0.592254]
+    MauntzKucherenkoSensitivityAlgorithm
+        S =  [0.205369,0.314775,0.698245]
+        T =  [0.220371,0.172107,0.567821]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 121-134
+
+.. code-block:: Python
+
+    print("Quasi Monte-Carlo sampling")
+    for estimator in ["Saltelli", "Martinez", "Jansen", "MauntzKucherenko"]:
+        (
+            computed_first_order,
+            computed_total_order,
+        ) = metaSAAlgorithm.runSamplingEstimator(
+            sample_size, estimator=estimator, sampling_method="QMC"
+        )
+        name = sobolAlgorithm.getClassName()
+        print(name)
+        print("    S = ", computed_first_order)
+        print("    T = ", computed_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Quasi Monte-Carlo sampling
+    MauntzKucherenkoSensitivityAlgorithm
+        S =  [0.147144,0.151326,0.624455]
+        T =  [0.212622,0.209087,0.643622]
+    MauntzKucherenkoSensitivityAlgorithm
+        S =  [0.147601,0.150976,0.629051]
+        T =  [0.210037,0.210799,0.640943]
+    MauntzKucherenkoSensitivityAlgorithm
+        S =  [0.151099,0.149846,0.632098]
+        T =  [0.20915,0.21105,0.635553]
+    MauntzKucherenkoSensitivityAlgorithm
+        S =  [0.159027,0.163209,0.636338]
+        T =  [0.212622,0.209087,0.643622]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 135-149
+
+.. code-block:: Python
+
+    print("Polynomial chaos")
+    sample_size = 500
+    (
+        computed_first_order,
+        computed_total_order,
+    ) = metaSAAlgorithm.runPolynomialChaosEstimator(
+        sample_size_train=sample_size,
+        sample_size_test=2,
+        total_degree=5,
+        hyperbolic_quasinorm=0.5,
+    )
+    print("    S = ", computed_first_order)
+    print("    T = ", computed_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Polynomial chaos
+        S =  [0.157895,0.157895,0.631579]
+        T =  [0.210526,0.210526,0.631579]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 150-152
+
+Define the metric
+-----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 154-164
+
+We consider the following accuracy metrics:
+* the vector or log relative errors for a given index (first order or total order),
+* the mean log relative error, as the mean of the LRE vector (first order or total order),
+* the average mean log relative error, as the mean of the first and total order mean log relative error.
+
+Larger LRE values are prefered.
+
+The first order (resp. total order) mean LRE represents the mean number of digits for all components
+of the first order indices (resp. total order indices).
+The average mean LRE represents the mean LRE for both first and total order indices.
+
+.. GENERATED FROM PYTHON SOURCE LINES 166-176
+
+.. code-block:: Python
+
+    S_LRE = ot.Point(dimension)
+    T_LRE = ot.Point(dimension)
+    for i in range(dimension):
+        S_LRE[i] = otb.ComputeLogRelativeError(
+            computed_first_order[i], exact_first_order[i]
+        )
+        T_LRE[i] = otb.ComputeLogRelativeError(
+            computed_total_order[i], exact_total_order[i]
+        )
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 177-180
+
+.. code-block:: Python
+
+    print("LRE S = ", S_LRE)
+    print("LRE T = ", T_LRE)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    LRE S =  [14.7136,14.5509,15.153]
+    LRE T =  [15.0349,14.8008,15.153]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 181-188
+
+.. code-block:: Python
+
+    mean_LRE_S = sum(S_LRE) / dimension
+    mean_LRE_T = sum(T_LRE) / dimension
+    mean_LRE = (mean_LRE_S + mean_LRE_T) / 2.0
+    print("Mean LRE S = %.2f" % (mean_LRE_S))
+    print("Mean LRE T = %.2f" % (mean_LRE_T))
+    print("Mean LRE = %.2f" % (mean_LRE))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Mean LRE S = 14.81
+    Mean LRE T = 15.00
+    Mean LRE = 14.90
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 189-190
+
+The digit per point ratio measure the number of digits relatively to the sample size. A greater value is prefered.
+
+.. GENERATED FROM PYTHON SOURCE LINES 190-192
+
+.. code-block:: Python
+
+    digit_per_point_ratio = mean_LRE / sample_size
+    print("Digit / point = %.3e" % (digit_per_point_ratio))
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Digit / point = 2.980e-02
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 0.034 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_benchmark_sensitivity_methods.ipynb <plot_benchmark_sensitivity_methods.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_benchmark_sensitivity_methods.py <plot_benchmark_sensitivity_methods.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_benchmark_sensitivity_methods.zip <plot_benchmark_sensitivity_methods.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borehole_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borehole_sensitivity.rst
new file mode 100644
index 00000000000..60250c68e5f
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borehole_sensitivity.rst
@@ -0,0 +1,340 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_borehole_sensitivity.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py:
+
+
+Benchmark the Borehole test function
+====================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-15
+
+.. code-block:: Python
+
+    problem = otb.BoreholeSensitivity()
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = Borehole
+    distribution = ComposedDistribution(Normal(mu = 0.1, sigma = 0.0161812), LogNormal(muLog = 7.71, sigmaLog = 1.0056, gamma = 0), Uniform(a = 63070, b = 115600), Uniform(a = 990, b = 1110), Uniform(a = 63.1, b = 116), Uniform(a = 700, b = 820), Uniform(a = 1120, b = 1680), Uniform(a = 9855, b = 12045), IndependentCopula(dimension = 8))
+    function = [rw,r,Tu,Hu,Tl,Hl,L,Kw]->[(2*pi_*Tu*(Hu-Hl))/(ln(r/rw)*(1+(2*L*Tu)/(ln(r/rw)*rw^2*Kw)+Tu/Tl))]
+    firstOrderIndices = [0.66,0,0,0.09,0,0.09,0.09,0.02]
+    totalOrderIndices = [0.69,0,0,0.11,0,0.11,0.1,0.02]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 16-19
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-21
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-24
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    exact_first_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=8 values=[0.66,0,0,0.09,0,0.09,0.09,0.02]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 25-28
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    exact_total_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=8 values=[0.69,0,0,0.11,0,0.11,0.1,0.02]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-31
+
+Plot the function
+-----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-34
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 34-39
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 40-48
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    full_sample = ot.Sample(size, 1 + dimension)
+    full_sample[:, range(dimension)] = inputDesign
+    full_sample[:, dimension] = outputDesign
+    full_description = list(inputDesign.getDescription())
+    full_description.append(outputDesign.getDescription()[0])
+    full_sample.setDescription(full_description)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 49-58
+
+.. code-block:: Python
+
+    marginal_distribution = ot.ComposedDistribution(
+        [
+            ot.KernelSmoothing().build(full_sample.getMarginal(i))
+            for i in range(1 + dimension)
+        ]
+    )
+    clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+    _ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)})
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_001.png
+   :alt: plot borehole sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-62
+
+.. code-block:: Python
+
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_002.png
+   :alt: plot borehole sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 63-65
+
+Perform sensitivity analysis
+----------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-68
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-73
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 10000
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-75
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 75-79
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 80-81
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 81-91
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    # Compute absolute error (the LRE cannot be computed,
+    # because S can be zero)
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  10000
+    Computed first order =  [0.673759,0.00700637,0.00702324,0.0955299,0.00698057,0.106366,0.0916606,0.0283122]
+    Exact first order =  [0.66,0,0,0.09,0,0.09,0.09,0.02]
+    Computed total order =  [0.699598,-5.69378e-05,-3.20159e-08,0.0973057,9.79799e-05,0.103265,0.095226,0.0247122]
+    Exact total order =  [0.69,0,0,0.11,0,0.11,0.1,0.02]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 92-94
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borehole_sensitivity_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-96
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 3.696 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_borehole_sensitivity.ipynb <plot_borehole_sensitivity.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_borehole_sensitivity.py <plot_borehole_sensitivity.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_borehole_sensitivity.zip <plot_borehole_sensitivity.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borgonovo_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borgonovo_sensitivity.rst
new file mode 100644
index 00000000000..b356b8c58bb
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_borgonovo_sensitivity.rst
@@ -0,0 +1,340 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_borgonovo_sensitivity.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py:
+
+
+Benchmark the Borgonovo test function
+=====================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-15
+
+.. code-block:: Python
+
+    problem = otb.BorgonovoSensitivity()
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = Borgonovo
+    distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3))
+    function = [x1,x2,x3]->[x1 * x2 + x3]
+    firstOrderIndices = [0.157895,0.157895,0.631579]
+    totalOrderIndices = [0.210526,0.210526,0.631579]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 16-19
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-21
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-24
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    exact_first_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=3 values=[0.157895,0.157895,0.631579]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 25-28
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    exact_total_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=3 values=[0.210526,0.210526,0.631579]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-31
+
+Plot the function
+-----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-34
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 34-39
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 40-48
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    full_sample = ot.Sample(size, 1 + dimension)
+    full_sample[:, range(dimension)] = inputDesign
+    full_sample[:, dimension] = outputDesign
+    full_description = list(inputDesign.getDescription())
+    full_description.append(outputDesign.getDescription()[0])
+    full_sample.setDescription(full_description)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 49-58
+
+.. code-block:: Python
+
+    marginal_distribution = ot.ComposedDistribution(
+        [
+            ot.KernelSmoothing().build(full_sample.getMarginal(i))
+            for i in range(1 + dimension)
+        ]
+    )
+    clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+    _ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_001.png
+   :alt: plot borgonovo sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-62
+
+.. code-block:: Python
+
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_002.png
+   :alt: plot borgonovo sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 63-65
+
+Perform sensitivity analysis
+----------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-68
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-73
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 10000
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-75
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 75-79
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 80-81
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 81-91
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    # Compute absolute error (the LRE cannot be computed,
+    # because S can be zero)
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  10000
+    Computed first order =  [0.166136,0.157381,0.623798]
+    Exact first order =  [0.157895,0.157895,0.631579]
+    Computed total order =  [0.207054,0.2159,0.623765]
+    Exact total order =  [0.210526,0.210526,0.631579]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 92-94
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_borgonovo_sensitivity_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-96
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 1.145 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_borgonovo_sensitivity.ipynb <plot_borgonovo_sensitivity.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_borgonovo_sensitivity.py <plot_borgonovo_sensitivity.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_borgonovo_sensitivity.zip <plot_borgonovo_sensitivity.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_convergence_ishigami.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_convergence_ishigami.rst
index bf50767a2b0..ceea9c95a27 100644
--- a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_convergence_ishigami.rst
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_convergence_ishigami.rst
@@ -463,7 +463,7 @@ Use polynomial chaos.
     Elapsed = 0.1 (s), Sample size = 160
     Elapsed = 0.6 (s), Sample size = 320
     Elapsed = 3.1 (s), Sample size = 640
-    Elapsed = 18.60 (s)
+    Elapsed = 18.70 (s)
 
 
 
@@ -483,7 +483,7 @@ Use polynomial chaos.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 44.837 seconds)
+   **Total running time of the script:** (0 minutes 45.054 seconds)
 
 
 .. _sphx_glr_download_auto_examples_sensitivity_problems_plot_convergence_ishigami.py:
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.rst
new file mode 100644
index 00000000000..e108acad3cb
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.rst
@@ -0,0 +1,340 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py:
+
+
+Benchmark the Dirichlet test function
+=====================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-15
+
+.. code-block:: Python
+
+    problem = otb.DirichletSensitivity()
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = Dirichlet
+    distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3))
+    function = class=PythonEvaluation name=DirichletFunction
+    firstOrderIndices = [0.525547,0.233577,0.131387]
+    totalOrderIndices = [0.620438,0.310219,0.182482]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 16-19
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-21
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-24
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    exact_first_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=3 values=[0.525547,0.233577,0.131387]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 25-28
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    exact_total_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=3 values=[0.620438,0.310219,0.182482]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-31
+
+Plot the function
+-----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-34
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 34-39
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 40-48
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    full_sample = ot.Sample(size, 1 + dimension)
+    full_sample[:, range(dimension)] = inputDesign
+    full_sample[:, dimension] = outputDesign
+    full_description = list(inputDesign.getDescription())
+    full_description.append(outputDesign.getDescription()[0])
+    full_sample.setDescription(full_description)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 49-58
+
+.. code-block:: Python
+
+    marginal_distribution = ot.ComposedDistribution(
+        [
+            ot.KernelSmoothing().build(full_sample.getMarginal(i))
+            for i in range(1 + dimension)
+        ]
+    )
+    clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+    _ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_001.png
+   :alt: plot dirichlet sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-62
+
+.. code-block:: Python
+
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_002.png
+   :alt: plot dirichlet sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 63-65
+
+Perform sensitivity analysis
+----------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-68
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-73
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 10000
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-75
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 75-79
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 80-81
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 81-91
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    # Compute absolute error (the LRE cannot be computed,
+    # because S can be zero)
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  10000
+    Computed first order =  [0.5332,0.240662,0.130402]
+    Exact first order =  [0.525547,0.233577,0.131387]
+    Computed total order =  [0.616112,0.301247,0.180961]
+    Exact total order =  [0.620438,0.310219,0.182482]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 92-94
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_dirichlet_sensitivity_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-96
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 2.273 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_dirichlet_sensitivity.ipynb <plot_dirichlet_sensitivity.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_dirichlet_sensitivity.py <plot_dirichlet_sensitivity.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_dirichlet_sensitivity.zip <plot_dirichlet_sensitivity.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_flood_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_flood_sensitivity.rst
new file mode 100644
index 00000000000..91a7016915e
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_flood_sensitivity.rst
@@ -0,0 +1,350 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_flood_sensitivity.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_flood_sensitivity.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_flood_sensitivity.py:
+
+
+Benchmark the Flooding test function
+====================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-14
+
+.. code-block:: Python
+
+    problem = otb.FloodingSensitivity()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 15-17
+
+.. code-block:: Python
+
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = Flooding
+    distribution = ComposedDistribution(TruncatedDistribution(Gumbel(beta = 558, gamma = 1013), bounds = [0, (19000.8) +inf[), TruncatedDistribution(Normal(mu = 30, sigma = 7.5), bounds = [0, (87.3797) +inf[), Uniform(a = 49, b = 51), Uniform(a = 54, b = 56), Uniform(a = 7, b = 9), Triangular(a = 55, m = 55.5, b = 56), Triangular(a = 4990, m = 5000, b = 5010), Triangular(a = 295, m = 300, b = 305), IndependentCopula(dimension = 8))
+    function = [Q,Ks,Zv,Zm,Hd,Zb,L,B]->[(Q / (Ks * B * sqrt((Zm - Zv) / L)))^(3.0 / 5.0) + Zv - Zb - Hd]
+    firstOrderIndices = [0.38,0.13,0.25,0,0.19,0.02,0,0]
+    totalOrderIndices = [0.4,0.15,0.25,0.01,0.19,0.02,0,0]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-21
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-23
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 23-26
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    exact_first_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=8 values=[0.38,0.13,0.25,0,0.19,0.02,0,0]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 27-30
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    exact_total_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=8 values=[0.4,0.15,0.25,0.01,0.19,0.02,0,0]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 31-33
+
+Plot the function
+-----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-36
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-41
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 42-50
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    full_sample = ot.Sample(size, 1 + dimension)
+    full_sample[:, range(dimension)] = inputDesign
+    full_sample[:, dimension] = outputDesign
+    full_description = list(inputDesign.getDescription())
+    full_description.append(outputDesign.getDescription()[0])
+    full_sample.setDescription(full_description)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 51-60
+
+.. code-block:: Python
+
+    marginal_distribution = ot.ComposedDistribution(
+        [
+            ot.KernelSmoothing().build(full_sample.getMarginal(i))
+            for i in range(1 + dimension)
+        ]
+    )
+    clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+    _ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)})
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_001.png
+   :alt: plot flood sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-64
+
+.. code-block:: Python
+
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_002.png
+   :alt: plot flood sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 65-67
+
+Perform sensitivity analysis
+----------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-70
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 70-75
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 10000
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-77
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-81
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 82-83
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-91
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  10000
+    Computed first order =  [0.371175,0.132331,0.26067,0.0122818,0.191486,0.0325675,0.00737938,0.00742294]
+    Exact first order =  [0.38,0.13,0.25,0,0.19,0.02,0,0]
+    Computed total order =  [0.413371,0.145399,0.24342,0.00473238,0.181701,0.0226346,5.5996e-06,0.000250305]
+    Exact total order =  [0.4,0.15,0.25,0.01,0.19,0.02,0,0]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 92-94
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_flood_sensitivity_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-96
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 2.451 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_flood_sensitivity.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_flood_sensitivity.ipynb <plot_flood_sensitivity.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_flood_sensitivity.py <plot_flood_sensitivity.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_flood_sensitivity.zip <plot_flood_sensitivity.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity.rst
new file mode 100644
index 00000000000..00565812625
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity.rst
@@ -0,0 +1,352 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py:
+
+
+Benchmark the gaussian product test function
+============================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-14
+
+.. code-block:: Python
+
+    problem = otb.GaussianProductSensitivity()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 15-17
+
+.. code-block:: Python
+
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = GaussianProduct
+    distribution = ComposedDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 2))
+    function = class=PythonEvaluation name=OpenTURNSPythonFunction
+    firstOrderIndices = [0,0]
+    totalOrderIndices = [1,1]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-21
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-23
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 23-26
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    print(exact_first_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [0,0]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 27-30
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    print(exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [1,1]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 31-33
+
+Plot the function
+-----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-36
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-41
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 42-50
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    full_sample = ot.Sample(size, 1 + dimension)
+    full_sample[:, range(dimension)] = inputDesign
+    full_sample[:, dimension] = outputDesign
+    full_description = list(inputDesign.getDescription())
+    full_description.append(outputDesign.getDescription()[0])
+    full_sample.setDescription(full_description)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 51-60
+
+.. code-block:: Python
+
+    marginal_distribution = ot.ComposedDistribution(
+        [
+            ot.KernelSmoothing().build(full_sample.getMarginal(i))
+            for i in range(1 + dimension)
+        ]
+    )
+    clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+    view = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_001.png
+   :alt: plot gaussian product sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-64
+
+.. code-block:: Python
+
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_002.png
+   :alt: plot gaussian product sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 65-67
+
+Perform sensitivity analysis
+----------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-70
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 70-75
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 10000
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-77
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-81
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 82-83
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-93
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    # Compute absolute error (the LRE cannot be computed,
+    # because S can be zero)
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  10000
+    Computed first order =  [0.00444004,-0.030873]
+    Exact first order =  [0,0]
+    Computed total order =  [1.02181,0.996161]
+    Exact total order =  [1,1]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 94-96
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_product_sensitivity_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 97-98
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 1.658 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_gaussian_product_sensitivity.ipynb <plot_gaussian_product_sensitivity.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_gaussian_product_sensitivity.py <plot_gaussian_product_sensitivity.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_gaussian_product_sensitivity.zip <plot_gaussian_product_sensitivity.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_sum.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_sum.rst
new file mode 100644
index 00000000000..80defe138fd
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gaussian_sum.rst
@@ -0,0 +1,352 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_gaussian_sum.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_gaussian_sum.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_sum.py:
+
+
+Benchmark the gaussian sum test function
+========================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-14
+
+.. code-block:: Python
+
+    problem = otb.GaussianSumSensitivity()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 15-17
+
+.. code-block:: Python
+
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = GaussianSum
+    distribution = ComposedDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 2))
+    function = class=PythonEvaluation name=OpenTURNSPythonFunction
+    firstOrderIndices = [0.5,0.5]
+    totalOrderIndices = [0.5,0.5]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-21
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-23
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 23-26
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    print(exact_first_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [0.5,0.5]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 27-30
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    print(exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [0.5,0.5]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 31-33
+
+Plot the function
+-----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-36
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-41
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 42-50
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    full_sample = ot.Sample(size, 1 + dimension)
+    full_sample[:, range(dimension)] = inputDesign
+    full_sample[:, dimension] = outputDesign
+    full_description = list(inputDesign.getDescription())
+    full_description.append(outputDesign.getDescription()[0])
+    full_sample.setDescription(full_description)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 51-60
+
+.. code-block:: Python
+
+    marginal_distribution = ot.ComposedDistribution(
+        [
+            ot.KernelSmoothing().build(full_sample.getMarginal(i))
+            for i in range(1 + dimension)
+        ]
+    )
+    clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+    view = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_001.png
+   :alt: plot gaussian sum
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-64
+
+.. code-block:: Python
+
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_002.png
+   :alt: plot gaussian sum
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 65-67
+
+Perform sensitivity analysis
+----------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-70
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 70-75
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 10000
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-77
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-81
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 82-83
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-93
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    # Compute absolute error (the LRE cannot be computed,
+    # because S can be zero)
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  10000
+    Computed first order =  [0.513373,0.504889]
+    Exact first order =  [0.5,0.5]
+    Computed total order =  [0.50317,0.494875]
+    Exact total order =  [0.5,0.5]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 94-96
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gaussian_sum_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 97-98
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 1.770 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_gaussian_sum.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_gaussian_sum.ipynb <plot_gaussian_sum.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_gaussian_sum.py <plot_gaussian_sum.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_gaussian_sum.zip <plot_gaussian_sum.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gsobol_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gsobol_sensitivity.rst
new file mode 100644
index 00000000000..eada954f850
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_gsobol_sensitivity.rst
@@ -0,0 +1,361 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_gsobol_sensitivity.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py:
+
+
+Benchmark the G-Sobol test function
+===================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-15
+
+.. code-block:: Python
+
+    problem = otb.GSobolSensitivity()
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = GSobol
+    distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3))
+    function = class=PythonEvaluation name=OpenTURNSPythonFunction
+    firstOrderIndices = [0.986712,0.00986712,9.86712e-05]
+    totalOrderIndices = [0.990034,0.0131566,0.000132]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 16-19
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-21
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-24
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    exact_first_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=3 values=[0.986712,0.00986712,9.86712e-05]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 25-28
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    print(exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [0.990034,0.0131566,0.000132]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-31
+
+Plot function
+-------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-34
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 34-39
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 40-48
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    full_sample = ot.Sample(size, 1 + dimension)
+    full_sample[:, range(dimension)] = inputDesign
+    full_sample[:, dimension] = outputDesign
+    full_description = list(inputDesign.getDescription())
+    full_description.append(outputDesign.getDescription()[0])
+    full_sample.setDescription(full_description)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 49-59
+
+.. code-block:: Python
+
+    marginal_distribution = ot.ComposedDistribution(
+        [
+            ot.KernelSmoothing().build(full_sample.getMarginal(i))
+            for i in range(1 + dimension)
+        ]
+    )
+    clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+    _ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_001.png
+   :alt: plot gsobol sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-61
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-66
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 1000
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-70
+
+.. code-block:: Python
+
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_002.png
+   :alt: plot gsobol sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 71-73
+
+Perform SA
+----------
+
+.. GENERATED FROM PYTHON SOURCE LINES 75-76
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-81
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 10000
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 82-83
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-87
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 88-89
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 89-99
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    # Compute absolute error (the LRE cannot be computed,
+    # because S can be zero)
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  10000
+    Computed first order =  [0.978665,0.021486,0.0124414]
+    Exact first order =  [0.986712,0.00986712,9.86712e-05]
+    Computed total order =  [0.979669,0.0104535,-4.12658e-05]
+    Exact total order =  [0.990034,0.0131566,0.000132]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 100-102
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_gsobol_sensitivity_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 103-104
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 2.334 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_gsobol_sensitivity.ipynb <plot_gsobol_sensitivity.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_gsobol_sensitivity.py <plot_gsobol_sensitivity.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_gsobol_sensitivity.zip <plot_gsobol_sensitivity.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_ishigami_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_ishigami_sensitivity.rst
new file mode 100644
index 00000000000..0ce0ecddc29
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_ishigami_sensitivity.rst
@@ -0,0 +1,352 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_ishigami_sensitivity.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py:
+
+
+Benchmark the Ishigami test function
+====================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-14
+
+.. code-block:: Python
+
+    problem = otb.IshigamiSensitivity()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 15-17
+
+.. code-block:: Python
+
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = Ishigami
+    distribution = ComposedDistribution(Uniform(a = -3.14159, b = 3.14159), Uniform(a = -3.14159, b = 3.14159), Uniform(a = -3.14159, b = 3.14159), IndependentCopula(dimension = 3))
+    function = ParametricEvaluation([X1,X2,X3,a,b]->[sin(X1) + a * sin(X2)^2 + b * X3^4 * sin(X1)], parameters positions=[3,4], parameters=[a : 7, b : 0.1], input positions=[0,1,2])
+    firstOrderIndices = [0.313905,0.442411,0]
+    totalOrderIndices = [0.557589,0.442411,0.243684]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-21
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-23
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 23-26
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    exact_first_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=3 values=[0.313905,0.442411,0]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 27-30
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    exact_total_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=3 values=[0.557589,0.442411,0.243684]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 31-33
+
+Plot the function
+-----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-36
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-41
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 42-50
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    full_sample = ot.Sample(size, 1 + dimension)
+    full_sample[:, range(dimension)] = inputDesign
+    full_sample[:, dimension] = outputDesign
+    full_description = list(inputDesign.getDescription())
+    full_description.append(outputDesign.getDescription()[0])
+    full_sample.setDescription(full_description)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 51-60
+
+.. code-block:: Python
+
+    marginal_distribution = ot.ComposedDistribution(
+        [
+            ot.KernelSmoothing().build(full_sample.getMarginal(i))
+            for i in range(1 + dimension)
+        ]
+    )
+    clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+    _ = otv.View(clouds, figure_kw={"figsize": (6.0, 6.0)})
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_001.png
+   :alt: plot ishigami sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-64
+
+.. code-block:: Python
+
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_002.png
+   :alt: plot ishigami sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 65-67
+
+Perform sensitivity analysis
+----------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-70
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 70-75
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 10000
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-77
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-81
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 82-83
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-93
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    # Compute absolute error (the LRE cannot be computed,
+    # because S can be zero)
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  10000
+    Computed first order =  [0.302745,0.460846,0.0066916]
+    Exact first order =  [0.313905,0.442411,0]
+    Computed total order =  [0.574996,0.427126,0.256689]
+    Exact total order =  [0.557589,0.442411,0.243684]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 94-96
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_ishigami_sensitivity_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 97-98
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 1.187 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_ishigami_sensitivity.ipynb <plot_ishigami_sensitivity.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_ishigami_sensitivity.py <plot_ishigami_sensitivity.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_ishigami_sensitivity.zip <plot_ishigami_sensitivity.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_morris_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_morris_sensitivity.rst
new file mode 100644
index 00000000000..3bc2afd5873
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_morris_sensitivity.rst
@@ -0,0 +1,330 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_morris_sensitivity.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_morris_sensitivity.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_morris_sensitivity.py:
+
+
+Benchmark the Morris test function
+==================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-15
+
+.. code-block:: Python
+
+    problem = otb.MorrisSensitivity()
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = Morris
+    distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 20))
+    function = class=PythonEvaluation name=MorrisFunction
+    firstOrderIndices = [0.08,0.08,0.06,0.08,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20
+    totalOrderIndices = [0.11,0.11,0.06,0.11,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 16-19
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-21
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-24
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    exact_first_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=20 values=[0.08,0.08,0.06,0.08,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 25-28
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    exact_total_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=20 values=[0.11,0.11,0.06,0.11,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-31
+
+Plot the function
+-----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-34
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 34-39
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 40-65
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    nbcolumns = 4
+    nbrows = int(dimension / nbcolumns)
+    grid = ot.GridLayout(nbrows, nbcolumns)
+    inputDescription = distribution.getDescription()
+    outputDescription = model.getOutputDescription()[0]
+    index = 0
+    for i in range(nbrows):
+        for j in range(nbcolumns):
+            graph = ot.Graph(
+                "n=%d" % (size), inputDescription[index], outputDescription, True, ""
+            )
+            sample = ot.Sample(size, 2)
+            sample[:, 0] = inputDesign[:, index]
+            sample[:, 1] = outputDesign[:, 0]
+            cloud = ot.Cloud(sample)
+            graph.add(cloud)
+            grid.setGraph(i, j, graph)
+            index += 1
+    _ = otv.View(grid, figure_kw={"figsize": (10.0, 10.0)})
+
+    # %
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_001.png
+         :alt: , n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200
+         :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_001.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_002.png
+         :alt: plot morris sensitivity
+         :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_002.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 66-68
+
+Perform sensitivity analysis
+----------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 70-71
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 71-76
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 30
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-78
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 78-82
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-84
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 84-94
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    # Compute absolute error (the LRE cannot be computed,
+    # because S can be zero)
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  30
+    Computed first order =  [0.0607395,0.20637,0.267428,0.262258,0.314109,0.61827,0.294943,0.439378,0.246433,0.274413,0.221509,0.18807,0.218819,0.228214,0.208058,0.215507,0.23585,0.21719,0.237087,0.214426]#20
+    Exact first order =  [0.08,0.08,0.06,0.08,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20
+    Computed total order =  [0.0311589,0.22498,0.219259,0.078154,-0.0539979,-0.0392477,-0.0216198,0.20086,0.311105,0.0877451,-0.0160964,-0.000247103,-0.0276497,0.000406818,-0.0165498,-0.0183569,-0.0308313,-0.0264689,-0.00283312,-0.00660521]#20
+    Exact total order =  [0.11,0.11,0.06,0.11,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-97
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_morris_sensitivity_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 98-99
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 16.944 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_morris_sensitivity.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_morris_sensitivity.ipynb <plot_morris_sensitivity.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_morris_sensitivity.py <plot_morris_sensitivity.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_morris_sensitivity.zip <plot_morris_sensitivity.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_nloscillator_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_nloscillator_sensitivity.rst
new file mode 100644
index 00000000000..a5b1eb10717
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_nloscillator_sensitivity.rst
@@ -0,0 +1,340 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_nloscillator_sensitivity.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py:
+
+
+Benchmark the NLOscillator test function
+========================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-15
+
+.. code-block:: Python
+
+    problem = otb.NLOscillatorSensitivity()
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = N.L. Oscillator
+    distribution = ComposedDistribution(class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=21.5 sigma=2.15 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=3.06308 sigmaLog=0.0997513 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=1.5 sigma=0.15 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=0.40049 sigmaLog=0.0997513 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.01 sigma=0.001 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-4.61015 sigmaLog=0.0997513 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=1 sigma=0.2 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-0.0196104 sigmaLog=0.198042 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.01 sigma=0.002 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-4.62478 sigmaLog=0.198042 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.05 sigma=0.02 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-3.06994 sigmaLog=0.385253 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.02 sigma=0.01 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-4.02359 sigmaLog=0.472381 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=100 sigma=10 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=4.6002 sigmaLog=0.0997513 gamma=0, IndependentCopula(dimension = 8))
+    function = class=PythonEvaluation name=OpenTURNSPythonFunction
+    firstOrderIndices = [0.4,0.03,0.09,0.18,0.12,0.05,0.05,0]
+    totalOrderIndices = [0.4,0.04,0.1,0.23,0.16,0.07,0.06,0.01]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 16-19
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-21
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-24
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    exact_first_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=8 values=[0.4,0.03,0.09,0.18,0.12,0.05,0.05,0]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 25-28
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    exact_total_order
+
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    class=Point name=Unnamed dimension=8 values=[0.4,0.04,0.1,0.23,0.16,0.07,0.06,0.01]
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-31
+
+Plot the function
+-----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-34
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 34-39
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 40-48
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    full_sample = ot.Sample(size, 1 + dimension)
+    full_sample[:, range(dimension)] = inputDesign
+    full_sample[:, dimension] = outputDesign
+    full_description = list(inputDesign.getDescription())
+    full_description.append(outputDesign.getDescription()[0])
+    full_sample.setDescription(full_description)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 49-58
+
+.. code-block:: Python
+
+    marginal_distribution = ot.ComposedDistribution(
+        [
+            ot.KernelSmoothing().build(full_sample.getMarginal(i))
+            for i in range(1 + dimension)
+        ]
+    )
+    clouds = ot.VisualTest.DrawPairsMarginals(full_sample, marginal_distribution)
+    _ = otv.View(clouds, figure_kw={"figsize": (10.0, 10.0)})
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_001.png
+   :alt: plot nloscillator sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-62
+
+.. code-block:: Python
+
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_002.png
+   :alt: plot nloscillator sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 63-65
+
+Perform sensitivity analysis
+----------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-68
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-73
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 10000
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-75
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 75-79
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 80-81
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 81-91
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    # Compute absolute error (the LRE cannot be computed,
+    # because S can be zero)
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  10000
+    Computed first order =  [0.374552,0.0347919,0.0853174,0.186246,0.120437,0.04356,0.0358369,0.00467826]
+    Exact first order =  [0.4,0.03,0.09,0.18,0.12,0.05,0.05,0]
+    Computed total order =  [0.375092,0.0778412,0.127126,0.279952,0.201389,0.0667096,0.0458004,0.00812321]
+    Exact total order =  [0.4,0.04,0.1,0.23,0.16,0.07,0.06,0.01]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 92-94
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_nloscillator_sensitivity_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-96
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 3.807 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_nloscillator_sensitivity.ipynb <plot_nloscillator_sensitivity.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_nloscillator_sensitivity.py <plot_nloscillator_sensitivity.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_nloscillator_sensitivity.zip <plot_nloscillator_sensitivity.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity.rst
new file mode 100644
index 00000000000..fde2ab72657
--- /dev/null
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity.rst
@@ -0,0 +1,330 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py:
+
+
+Benchmark the Oakley-O'Hagan test function
+==========================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-11
+
+.. code-block:: Python
+
+    import openturns as ot
+    import otbenchmark as otb
+    import openturns.viewer as otv
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-15
+
+.. code-block:: Python
+
+    problem = otb.OakleyOHaganSensitivity()
+    print(problem)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    name = Oakley-O'Hagan
+    distribution = ComposedDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 15))
+    function = class=PythonEvaluation name=OpenTURNSPythonFunction
+    firstOrderIndices = [0,0,0,0,0,0.02,0.02,0.03,0.05,0.01,0.1,0.14,0.1,0.11,0.12]#15
+    totalOrderIndices = [0.06,0.06,0.04,0.05,0.02,0.04,0.06,0.08,0.1,0.04,0.15,0.15,0.14,0.14,0.16]#15
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 16-19
+
+.. code-block:: Python
+
+    distribution = problem.getInputDistribution()
+    model = problem.getFunction()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-21
+
+Exact first and total order
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-24
+
+.. code-block:: Python
+
+    exact_first_order = problem.getFirstOrderIndices()
+    print(exact_first_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [0,0,0,0,0,0.02,0.02,0.03,0.05,0.01,0.1,0.14,0.1,0.11,0.12]#15
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 25-31
+
+.. code-block:: Python
+
+    exact_total_order = problem.getTotalOrderIndices()
+    print(exact_total_order)
+
+
+    # ## Plot the function
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [0.06,0.06,0.04,0.05,0.02,0.04,0.06,0.08,0.1,0.04,0.15,0.15,0.14,0.14,0.16]#15
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 32-33
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-38
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 200
+    inputDesign = ot.MonteCarloExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 39-60
+
+.. code-block:: Python
+
+    dimension = distribution.getDimension()
+    nbcolumns = 5
+    nbrows = int(dimension / nbcolumns)
+    grid = ot.GridLayout(nbrows, nbcolumns)
+    inputDescription = distribution.getDescription()
+    outputDescription = model.getOutputDescription()[0]
+    index = 0
+    for i in range(nbrows):
+        for j in range(nbcolumns):
+            graph = ot.Graph(
+                "n=%d" % (size), inputDescription[index], outputDescription, True, ""
+            )
+            sample = ot.Sample(size, 2)
+            sample[:, 0] = inputDesign[:, index]
+            sample[:, 1] = outputDesign[:, 0]
+            cloud = ot.Cloud(sample)
+            graph.add(cloud)
+            grid.setGraph(i, j, graph)
+            index += 1
+    _ = otv.View(grid, figure_kw={"figsize": (10.0, 10.0)})
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_001.png
+   :alt: , n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-64
+
+.. code-block:: Python
+
+    output_distribution = ot.KernelSmoothing().build(outputDesign)
+    _ = otv.View(output_distribution.drawPDF())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_002.png
+   :alt: plot oakleyohagan sensitivity
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 65-66
+
+## Perform sensitivity analysis
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-69
+
+Create X/Y data
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-74
+
+.. code-block:: Python
+
+    ot.RandomGenerator.SetSeed(0)
+    size = 1000
+    inputDesign = ot.SobolIndicesExperiment(distribution, size).generate()
+    outputDesign = model(inputDesign)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 75-76
+
+Compute first order indices using the Saltelli estimator
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-80
+
+.. code-block:: Python
+
+    sensitivityAnalysis = ot.SaltelliSensitivityAlgorithm(inputDesign, outputDesign, size)
+    computed_first_order = sensitivityAnalysis.getFirstOrderIndices()
+    computed_total_order = sensitivityAnalysis.getTotalOrderIndices()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 81-82
+
+Compare with exact results
+
+.. GENERATED FROM PYTHON SOURCE LINES 82-92
+
+.. code-block:: Python
+
+    print("Sample size : ", size)
+    # First order
+    # Compute absolute error (the LRE cannot be computed,
+    # because S can be zero)
+    print("Computed first order = ", computed_first_order)
+    print("Exact first order = ", exact_first_order)
+    # Total order
+    print("Computed total order = ", computed_total_order)
+    print("Exact total order = ", exact_total_order)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Sample size :  1000
+    Computed first order =  [0.00686154,0.0173649,0.0121877,-0.0153947,-0.0223964,0.0038293,0.0373239,0.014391,0.0568532,0.0057647,0.0867005,0.162502,0.0882588,0.0706715,0.108779]#15
+    Exact first order =  [0,0,0,0,0,0.02,0.02,0.03,0.05,0.01,0.1,0.14,0.1,0.11,0.12]#15
+    Computed total order =  [0.0565808,0.0625981,0.0461935,0.0758801,0.0356766,0.028928,0.058367,0.101891,0.087846,0.0501691,0.128598,0.146953,0.125396,0.149566,0.16276]#15
+    Exact total order =  [0.06,0.06,0.04,0.05,0.02,0.04,0.06,0.08,0.1,0.04,0.15,0.15,0.14,0.14,0.16]#15
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 93-95
+
+.. code-block:: Python
+
+    _ = otv.View(sensitivityAnalysis.draw())
+
+
+
+
+.. image-sg:: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_003.png
+   :alt: Sobol' indices - SaltelliSensitivityAlgorithm
+   :srcset: /auto_examples/sensitivity_problems/images/sphx_glr_plot_oakleyohagan_sensitivity_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 96-97
+
+.. code-block:: Python
+
+    otv.View.ShowAll()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 1.088 seconds)
+
+
+.. _sphx_glr_download_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_oakleyohagan_sensitivity.ipynb <plot_oakleyohagan_sensitivity.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_oakleyohagan_sensitivity.py <plot_oakleyohagan_sensitivity.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_oakleyohagan_sensitivity.zip <plot_oakleyohagan_sensitivity.zip>`
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_print_problems.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_print_problems.rst
index 0238c66b83c..1cf29e6d0cc 100644
--- a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_print_problems.rst
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/plot_print_problems.rst
@@ -178,7 +178,7 @@ For each problem in the benchmark, print the problem name and the exact Sobol' i
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.165 seconds)
+   **Total running time of the script:** (0 minutes 0.252 seconds)
 
 
 .. _sphx_glr_download_auto_examples_sensitivity_problems_plot_print_problems.py:
diff --git a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/sg_execution_times.rst b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/sg_execution_times.rst
index 8723708d561..3608e6f635a 100644
--- a/otbenchmark/master/_sources/auto_examples/sensitivity_problems/sg_execution_times.rst
+++ b/otbenchmark/master/_sources/auto_examples/sensitivity_problems/sg_execution_times.rst
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:45.002** total execution time for 2 files **from auto_examples/sensitivity_problems**:
+**01:23.694** total execution time for 14 files **from auto_examples/sensitivity_problems**:
 
 .. container::
 
@@ -33,8 +33,44 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_convergence_ishigami.py` (``plot_convergence_ishigami.py``)
-     - 00:44.837
+     - 00:45.054
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_morris_sensitivity.py` (``plot_morris_sensitivity.py``)
+     - 00:16.944
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py` (``plot_nloscillator_sensitivity.py``)
+     - 00:03.807
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py` (``plot_borehole_sensitivity.py``)
+     - 00:03.696
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_flood_sensitivity.py` (``plot_flood_sensitivity.py``)
+     - 00:02.451
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py` (``plot_gsobol_sensitivity.py``)
+     - 00:02.334
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py` (``plot_dirichlet_sensitivity.py``)
+     - 00:02.273
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_sum.py` (``plot_gaussian_sum.py``)
+     - 00:01.770
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py` (``plot_gaussian_product_sensitivity.py``)
+     - 00:01.658
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py` (``plot_ishigami_sensitivity.py``)
+     - 00:01.187
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py` (``plot_borgonovo_sensitivity.py``)
+     - 00:01.145
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py` (``plot_oakleyohagan_sensitivity.py``)
+     - 00:01.088
      - 0.0
    * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_print_problems.py` (``plot_print_problems.py``)
-     - 00:00.165
+     - 00:00.252
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py` (``plot_benchmark_sensitivity_methods.py``)
+     - 00:00.034
      - 0.0
diff --git a/otbenchmark/master/_sources/auto_examples/sg_execution_times.rst b/otbenchmark/master/_sources/auto_examples/sg_execution_times.rst
index 49da92f6963..f3616dc40f5 100644
--- a/otbenchmark/master/_sources/auto_examples/sg_execution_times.rst
+++ b/otbenchmark/master/_sources/auto_examples/sg_execution_times.rst
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:24.333** total execution time for 6 files **from auto_examples**:
+**00:23.239** total execution time for 6 files **from auto_examples**:
 
 .. container::
 
@@ -33,20 +33,20 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_3d.py` (``plot_crosscut_distribution_3d.py``)
-     - 00:08.366
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_plot_ConditionalDistribution_Demo.py` (``plot_ConditionalDistribution_Demo.py``)
-     - 00:04.619
+     - 00:07.724
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_methodFactory.py` (``plot_methodFactory.py``)
-     - 00:04.439
+     - 00:07.465
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_2d.py` (``plot_crosscut_distribution_2d.py``)
-     - 00:03.997
+   * - :ref:`sphx_glr_auto_examples_plot_ConditionalDistribution_Demo.py` (``plot_ConditionalDistribution_Demo.py``)
+     - 00:02.577
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_convergence_reliability_mc.py` (``plot_convergence_reliability_mc.py``)
-     - 00:02.174
+     - 00:02.177
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_2d.py` (``plot_crosscut_distribution_2d.py``)
+     - 00:02.066
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_CrossCutFunction_Demo.py` (``plot_CrossCutFunction_Demo.py``)
-     - 00:00.738
+     - 00:01.229
      - 0.0
diff --git a/otbenchmark/master/_sources/index.rst b/otbenchmark/master/_sources/index.rst
index 01b259514a6..9fc8066f0f0 100644
--- a/otbenchmark/master/_sources/index.rst
+++ b/otbenchmark/master/_sources/index.rst
@@ -10,14 +10,6 @@ User documentation
    user_manual/user_manual
    ../auto_examples/index.rst
 
-Developer guide 
----------------
-
-.. toctree::
-   :maxdepth: 1 
-
-   developer_guide/developer_guide   
-
 Indices and tables
 ==================
 
diff --git a/otbenchmark/master/_sources/sg_execution_times.rst b/otbenchmark/master/_sources/sg_execution_times.rst
index d91e6aab675..2a0d646c170 100644
--- a/otbenchmark/master/_sources/sg_execution_times.rst
+++ b/otbenchmark/master/_sources/sg_execution_times.rst
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**01:09.335** total execution time for 8 files **from all galleries**:
+**01:46.932** total execution time for 20 files **from all galleries**:
 
 .. container::
 
@@ -33,26 +33,62 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_convergence_ishigami.py` (``examples/sensitivity_problems/plot_convergence_ishigami.py``)
-     - 00:44.837
+     - 00:45.054
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_morris_sensitivity.py` (``examples/sensitivity_problems/plot_morris_sensitivity.py``)
+     - 00:16.944
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_3d.py` (``examples/plot_crosscut_distribution_3d.py``)
-     - 00:08.366
+     - 00:07.724
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_methodFactory.py` (``examples/plot_methodFactory.py``)
+     - 00:07.465
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_nloscillator_sensitivity.py` (``examples/sensitivity_problems/plot_nloscillator_sensitivity.py``)
+     - 00:03.807
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borehole_sensitivity.py` (``examples/sensitivity_problems/plot_borehole_sensitivity.py``)
+     - 00:03.696
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_ConditionalDistribution_Demo.py` (``examples/plot_ConditionalDistribution_Demo.py``)
-     - 00:04.619
+     - 00:02.577
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_plot_methodFactory.py` (``examples/plot_methodFactory.py``)
-     - 00:04.439
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_flood_sensitivity.py` (``examples/sensitivity_problems/plot_flood_sensitivity.py``)
+     - 00:02.451
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_2d.py` (``examples/plot_crosscut_distribution_2d.py``)
-     - 00:03.997
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gsobol_sensitivity.py` (``examples/sensitivity_problems/plot_gsobol_sensitivity.py``)
+     - 00:02.334
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_dirichlet_sensitivity.py` (``examples/sensitivity_problems/plot_dirichlet_sensitivity.py``)
+     - 00:02.273
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_convergence_reliability_mc.py` (``examples/plot_convergence_reliability_mc.py``)
-     - 00:02.174
+     - 00:02.177
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_crosscut_distribution_2d.py` (``examples/plot_crosscut_distribution_2d.py``)
+     - 00:02.066
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_sum.py` (``examples/sensitivity_problems/plot_gaussian_sum.py``)
+     - 00:01.770
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_gaussian_product_sensitivity.py` (``examples/sensitivity_problems/plot_gaussian_product_sensitivity.py``)
+     - 00:01.658
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_CrossCutFunction_Demo.py` (``examples/plot_CrossCutFunction_Demo.py``)
-     - 00:00.738
+     - 00:01.229
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_ishigami_sensitivity.py` (``examples/sensitivity_problems/plot_ishigami_sensitivity.py``)
+     - 00:01.187
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_borgonovo_sensitivity.py` (``examples/sensitivity_problems/plot_borgonovo_sensitivity.py``)
+     - 00:01.145
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_oakleyohagan_sensitivity.py` (``examples/sensitivity_problems/plot_oakleyohagan_sensitivity.py``)
+     - 00:01.088
      - 0.0
    * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_print_problems.py` (``examples/sensitivity_problems/plot_print_problems.py``)
-     - 00:00.165
+     - 00:00.252
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_sensitivity_problems_plot_benchmark_sensitivity_methods.py` (``examples/sensitivity_problems/plot_benchmark_sensitivity_methods.py``)
+     - 00:00.034
      - 0.0
diff --git a/otbenchmark/master/auto_examples/index.html b/otbenchmark/master/auto_examples/index.html
index 6a833f3e79a..333a577beb1 100644
--- a/otbenchmark/master/auto_examples/index.html
+++ b/otbenchmark/master/auto_examples/index.html
@@ -75,9 +75,6 @@ <h3><a href="../index.html">Table of Contents</a></h3>
 <li class="toctree-l2"><a class="reference internal" href="sensitivity_problems/index.html">Sensitivity examples</a></li>
 </ul>
 </li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
@@ -143,9 +140,45 @@ <h1>Sensitivity examples<a class="headerlink" href="#sensitivity-examples" title
 <div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Print the sensitivity analysis problems"><img alt="" src="../_images/sphx_glr_plot_print_problems_thumb.png" />
 <p><a class="reference internal" href="sensitivity_problems/plot_print_problems.html#sphx-glr-auto-examples-sensitivity-problems-plot-print-problems-py"><span class="std std-ref">Print the sensitivity analysis problems</span></a></p>
   <div class="sphx-glr-thumbnail-title">Print the sensitivity analysis problems</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Flooding test function"><img alt="" src="../_images/sphx_glr_plot_flood_sensitivity_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_flood_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-flood-sensitivity-py"><span class="std std-ref">Benchmark the Flooding test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Flooding test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Borgonovo test function"><img alt="" src="../_images/sphx_glr_plot_borgonovo_sensitivity_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_borgonovo_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-borgonovo-sensitivity-py"><span class="std std-ref">Benchmark the Borgonovo test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Borgonovo test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Ishigami test function"><img alt="" src="../_images/sphx_glr_plot_ishigami_sensitivity_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_ishigami_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-ishigami-sensitivity-py"><span class="std std-ref">Benchmark the Ishigami test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Ishigami test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Dirichlet test function"><img alt="" src="../_images/sphx_glr_plot_dirichlet_sensitivity_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_dirichlet_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-dirichlet-sensitivity-py"><span class="std std-ref">Benchmark the Dirichlet test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Dirichlet test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Borehole test function"><img alt="" src="../_images/sphx_glr_plot_borehole_sensitivity_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_borehole_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-borehole-sensitivity-py"><span class="std std-ref">Benchmark the Borehole test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Borehole test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the NLOscillator test function"><img alt="" src="../_images/sphx_glr_plot_nloscillator_sensitivity_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_nloscillator_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-nloscillator-sensitivity-py"><span class="std std-ref">Benchmark the NLOscillator test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the NLOscillator test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the gaussian sum test function"><img alt="" src="../_images/sphx_glr_plot_gaussian_sum_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_gaussian_sum.html#sphx-glr-auto-examples-sensitivity-problems-plot-gaussian-sum-py"><span class="std std-ref">Benchmark the gaussian sum test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the gaussian sum test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the gaussian product test function"><img alt="" src="../_images/sphx_glr_plot_gaussian_product_sensitivity_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_gaussian_product_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-gaussian-product-sensitivity-py"><span class="std std-ref">Benchmark the gaussian product test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the gaussian product test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Morris test function"><img alt="" src="../_images/sphx_glr_plot_morris_sensitivity_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_morris_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-morris-sensitivity-py"><span class="std std-ref">Benchmark the Morris test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Morris test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Oakley-O&#x27;Hagan test function"><img alt="" src="../_images/sphx_glr_plot_oakleyohagan_sensitivity_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_oakleyohagan_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-oakleyohagan-sensitivity-py"><span class="std std-ref">Benchmark the Oakley-O’Hagan test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Oakley-O'Hagan test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the G-Sobol test function"><img alt="" src="../_images/sphx_glr_plot_gsobol_sensitivity_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_gsobol_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-gsobol-sensitivity-py"><span class="std std-ref">Benchmark the G-Sobol test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the G-Sobol test function</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Convergence of estimators on Ishigami"><img alt="" src="../_images/sphx_glr_plot_convergence_ishigami_thumb.png" />
 <p><a class="reference internal" href="sensitivity_problems/plot_convergence_ishigami.html#sphx-glr-auto-examples-sensitivity-problems-plot-convergence-ishigami-py"><span class="std std-ref">Convergence of estimators on Ishigami</span></a></p>
   <div class="sphx-glr-thumbnail-title">Convergence of estimators on Ishigami</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark sensitivity analysis methods"><img alt="" src="../_images/sphx_glr_plot_benchmark_sensitivity_methods_thumb.png" />
+<p><a class="reference internal" href="sensitivity_problems/plot_benchmark_sensitivity_methods.html#sphx-glr-auto-examples-sensitivity-problems-plot-benchmark-sensitivity-methods-py"><span class="std std-ref">Benchmark sensitivity analysis methods</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark sensitivity analysis methods</div>
 </div></div><div class="toctree-wrapper compound">
 </div>
 <div class="sphx-glr-footer sphx-glr-footer-gallery docutils container">
diff --git a/otbenchmark/master/auto_examples/plot_ConditionalDistribution_Demo.html b/otbenchmark/master/auto_examples/plot_ConditionalDistribution_Demo.html
index 797d0477c2b..1237c052d02 100644
--- a/otbenchmark/master/auto_examples/plot_ConditionalDistribution_Demo.html
+++ b/otbenchmark/master/auto_examples/plot_ConditionalDistribution_Demo.html
@@ -73,9 +73,6 @@ <h3><a href="../index.html">Table of Contents</a></h3>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
@@ -191,7 +188,7 @@ <h2>Conditional distribution of a three dimensional mixture<a class="headerlink"
 <img src="../_images/sphx_glr_plot_ConditionalDistribution_Demo_002.png" srcset="../_images/sphx_glr_plot_ConditionalDistribution_Demo_002.png" alt="[X0,X1] iso-PDF" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.619 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.577 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-conditionaldistribution-demo-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/7a35bce75fac8ff6e6c017226f4b180d/plot_ConditionalDistribution_Demo.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_ConditionalDistribution_Demo.ipynb</span></code></a></p>
diff --git a/otbenchmark/master/auto_examples/plot_CrossCutFunction_Demo.html b/otbenchmark/master/auto_examples/plot_CrossCutFunction_Demo.html
index fb5a008eaa1..3bcd4c258ef 100644
--- a/otbenchmark/master/auto_examples/plot_CrossCutFunction_Demo.html
+++ b/otbenchmark/master/auto_examples/plot_CrossCutFunction_Demo.html
@@ -73,9 +73,6 @@ <h3><a href="../index.html">Table of Contents</a></h3>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
@@ -194,7 +191,7 @@ <h2>Compute the bounds of the domain<a class="headerlink" href="#compute-the-bou
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.738 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.229 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-crosscutfunction-demo-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/4de1f2ba1d3582e728314b3cb2b74c75/plot_CrossCutFunction_Demo.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_CrossCutFunction_Demo.ipynb</span></code></a></p>
diff --git a/otbenchmark/master/auto_examples/plot_convergence_reliability_mc.html b/otbenchmark/master/auto_examples/plot_convergence_reliability_mc.html
index 5d9321c4132..dcad6bff8ec 100644
--- a/otbenchmark/master/auto_examples/plot_convergence_reliability_mc.html
+++ b/otbenchmark/master/auto_examples/plot_convergence_reliability_mc.html
@@ -73,9 +73,6 @@ <h3><a href="../index.html">Table of Contents</a></h3>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
@@ -182,7 +179,7 @@ <h3 id="searchlabel">Quick search</h3>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">ComputeProbabilityFromMonteCarlo</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Number of function calls = 1842 , Pf = 0.0765 , 95.0 % confidence interval :[0.0616,0.0915]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Number of function calls = 1978 , Pf = 0.0713 , 95.0 % confidence interval :[0.0573,0.0852]
 </pre></div>
 </div>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">numberOfPoints</span> <span class="o">=</span> <span class="mi">15</span>  <span class="c1"># Number of atomic experiments</span>
@@ -213,160 +210,160 @@ <h3 id="searchlabel">Quick search</h3>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
 Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
 Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978]
-Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978]
+Number of function calls = 4 , Pf = 0.5000 , 95.0 % confidence interval :[0.0100,0.9900]
+Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
+Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
 Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
-Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978]
 Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
 Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
 Number of function calls = 4 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
-Number of function calls = 4 , Pf = 0.2500 , 95.0 % confidence interval :[-0.2978,0.7978]
-Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123]
-Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123]
-Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
 Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
 Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123]
+Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
 Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123]
-Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123]
-Number of function calls = 8 , Pf = 0.2500 , 95.0 % confidence interval :[-0.1374,0.6374]
+Number of function calls = 8 , Pf = 0.3750 , 95.0 % confidence interval :[-0.0026,0.7526]
+Number of function calls = 8 , Pf = 0.2500 , 95.0 % confidence interval :[-0.0501,0.5501]
+Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
 Number of function calls = 8 , Pf = 0.2500 , 95.0 % confidence interval :[-0.1374,0.6374]
 Number of function calls = 8 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
-Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
-Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094]
-Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
+Number of function calls = 8 , Pf = 0.1250 , 95.0 % confidence interval :[-0.1623,0.4123]
 Number of function calls = 16 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0781,0.3281]
 Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094]
 Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094]
-Number of function calls = 16 , Pf = 0.2500 , 95.0 % confidence interval :[-0.0239,0.5239]
 Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094]
-Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
+Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094]
+Number of function calls = 16 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0370,0.2870]
+Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094]
+Number of function calls = 16 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0844,0.2094]
+Number of function calls = 16 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0781,0.3281]
 Number of function calls = 16 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
 Number of function calls = 32 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0430,0.1055]
-Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0161,0.2036]
-Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664]
+Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0321,0.2196]
 Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664]
+Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0321,0.2196]
+Number of function calls = 32 , Pf = 0.0000 , 95.0 % confidence interval :[0.0000,0.0000]
+Number of function calls = 32 , Pf = 0.1875 , 95.0 % confidence interval :[0.0390,0.3360]
+Number of function calls = 32 , Pf = 0.0938 , 95.0 % confidence interval :[-0.0321,0.2196]
 Number of function calls = 32 , Pf = 0.1562 , 95.0 % confidence interval :[-0.0025,0.3150]
-Number of function calls = 32 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0430,0.1055]
-Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664]
-Number of function calls = 32 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0430,0.1055]
-Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0214,0.1464]
 Number of function calls = 32 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0414,0.1664]
-Number of function calls = 64 , Pf = 0.1406 , 95.0 % confidence interval :[0.0335,0.2477]
-Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0102,0.1773]
-Number of function calls = 64 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0109,0.1359]
-Number of function calls = 64 , Pf = 0.1094 , 95.0 % confidence interval :[0.0138,0.2049]
-Number of function calls = 64 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0212,0.0837]
-Number of function calls = 64 , Pf = 0.1719 , 95.0 % confidence interval :[0.0675,0.2762]
+Number of function calls = 32 , Pf = 0.1250 , 95.0 % confidence interval :[-0.0186,0.2686]
+Number of function calls = 64 , Pf = 0.0781 , 95.0 % confidence interval :[-0.0035,0.1598]
+Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0048,0.1827]
 Number of function calls = 64 , Pf = 0.0469 , 95.0 % confidence interval :[-0.0171,0.1108]
+Number of function calls = 64 , Pf = 0.1562 , 95.0 % confidence interval :[0.0440,0.2685]
 Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0048,0.1827]
-Number of function calls = 64 , Pf = 0.0625 , 95.0 % confidence interval :[-0.0109,0.1359]
-Number of function calls = 64 , Pf = 0.0781 , 95.0 % confidence interval :[-0.0035,0.1598]
-Number of function calls = 128 , Pf = 0.0937 , 95.0 % confidence interval :[0.0308,0.1567]
-Number of function calls = 128 , Pf = 0.0781 , 95.0 % confidence interval :[0.0204,0.1359]
-Number of function calls = 128 , Pf = 0.0781 , 95.0 % confidence interval :[0.0204,0.1359]
-Number of function calls = 128 , Pf = 0.1016 , 95.0 % confidence interval :[0.0363,0.1669]
-Number of function calls = 128 , Pf = 0.1172 , 95.0 % confidence interval :[0.0475,0.1869]
-Number of function calls = 128 , Pf = 0.0703 , 95.0 % confidence interval :[0.0154,0.1252]
+Number of function calls = 64 , Pf = 0.0469 , 95.0 % confidence interval :[-0.0171,0.1108]
+Number of function calls = 64 , Pf = 0.1719 , 95.0 % confidence interval :[0.0589,0.2849]
+Number of function calls = 64 , Pf = 0.0781 , 95.0 % confidence interval :[0.0024,0.1538]
+Number of function calls = 64 , Pf = 0.0938 , 95.0 % confidence interval :[0.0102,0.1773]
+Number of function calls = 64 , Pf = 0.0469 , 95.0 % confidence interval :[-0.0171,0.1108]
+Number of function calls = 128 , Pf = 0.1094 , 95.0 % confidence interval :[0.0418,0.1769]
+Number of function calls = 128 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0059,0.0684]
+Number of function calls = 128 , Pf = 0.1250 , 95.0 % confidence interval :[0.0548,0.1952]
+Number of function calls = 128 , Pf = 0.0312 , 95.0 % confidence interval :[-0.0059,0.0684]
+Number of function calls = 128 , Pf = 0.0469 , 95.0 % confidence interval :[0.0017,0.0921]
 Number of function calls = 128 , Pf = 0.0391 , 95.0 % confidence interval :[-0.0023,0.0804]
-Number of function calls = 128 , Pf = 0.0859 , 95.0 % confidence interval :[0.0255,0.1463]
-Number of function calls = 128 , Pf = 0.0547 , 95.0 % confidence interval :[0.0060,0.1034]
-Number of function calls = 128 , Pf = 0.0938 , 95.0 % confidence interval :[0.0327,0.1548]
-Number of function calls = 256 , Pf = 0.0781 , 95.0 % confidence interval :[0.0380,0.1182]
+Number of function calls = 128 , Pf = 0.0859 , 95.0 % confidence interval :[0.0275,0.1444]
+Number of function calls = 128 , Pf = 0.1172 , 95.0 % confidence interval :[0.0492,0.1852]
+Number of function calls = 128 , Pf = 0.0703 , 95.0 % confidence interval :[0.0154,0.1252]
+Number of function calls = 128 , Pf = 0.1016 , 95.0 % confidence interval :[0.0381,0.1650]
+Number of function calls = 256 , Pf = 0.0820 , 95.0 % confidence interval :[0.0417,0.1224]
 Number of function calls = 256 , Pf = 0.0859 , 95.0 % confidence interval :[0.0432,0.1286]
-Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0286,0.1042]
-Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0286,0.1042]
-Number of function calls = 256 , Pf = 0.0742 , 95.0 % confidence interval :[0.0344,0.1141]
-Number of function calls = 256 , Pf = 0.0742 , 95.0 % confidence interval :[0.0351,0.1133]
-Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0286,0.1042]
-Number of function calls = 256 , Pf = 0.0586 , 95.0 % confidence interval :[0.0238,0.0934]
-Number of function calls = 256 , Pf = 0.1016 , 95.0 % confidence interval :[0.0554,0.1477]
-Number of function calls = 256 , Pf = 0.0977 , 95.0 % confidence interval :[0.0536,0.1417]
-Number of function calls = 512 , Pf = 0.0508 , 95.0 % confidence interval :[0.0276,0.0740]
+Number of function calls = 256 , Pf = 0.0781 , 95.0 % confidence interval :[0.0373,0.1190]
+Number of function calls = 256 , Pf = 0.0625 , 95.0 % confidence interval :[0.0266,0.0984]
+Number of function calls = 256 , Pf = 0.0781 , 95.0 % confidence interval :[0.0380,0.1182]
+Number of function calls = 256 , Pf = 0.0352 , 95.0 % confidence interval :[0.0074,0.0630]
+Number of function calls = 256 , Pf = 0.0664 , 95.0 % confidence interval :[0.0302,0.1026]
+Number of function calls = 256 , Pf = 0.0547 , 95.0 % confidence interval :[0.0202,0.0891]
+Number of function calls = 256 , Pf = 0.0625 , 95.0 % confidence interval :[0.0258,0.0992]
+Number of function calls = 256 , Pf = 0.0703 , 95.0 % confidence interval :[0.0315,0.1092]
 Number of function calls = 512 , Pf = 0.0723 , 95.0 % confidence interval :[0.0450,0.0996]
-Number of function calls = 512 , Pf = 0.0762 , 95.0 % confidence interval :[0.0482,0.1042]
-Number of function calls = 512 , Pf = 0.0645 , 95.0 % confidence interval :[0.0390,0.0900]
-Number of function calls = 512 , Pf = 0.0645 , 95.0 % confidence interval :[0.0381,0.0908]
-Number of function calls = 512 , Pf = 0.0762 , 95.0 % confidence interval :[0.0490,0.1034]
-Number of function calls = 512 , Pf = 0.0586 , 95.0 % confidence interval :[0.0334,0.0838]
-Number of function calls = 512 , Pf = 0.1055 , 95.0 % confidence interval :[0.0729,0.1380]
-Number of function calls = 512 , Pf = 0.0781 , 95.0 % confidence interval :[0.0498,0.1065]
-Number of function calls = 512 , Pf = 0.0840 , 95.0 % confidence interval :[0.0546,0.1134]
-Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0639,0.1060]
+Number of function calls = 512 , Pf = 0.0605 , 95.0 % confidence interval :[0.0358,0.0852]
+Number of function calls = 512 , Pf = 0.0996 , 95.0 % confidence interval :[0.0686,0.1306]
+Number of function calls = 512 , Pf = 0.0938 , 95.0 % confidence interval :[0.0628,0.1247]
+Number of function calls = 512 , Pf = 0.0742 , 95.0 % confidence interval :[0.0471,0.1013]
+Number of function calls = 512 , Pf = 0.0723 , 95.0 % confidence interval :[0.0450,0.0996]
+Number of function calls = 512 , Pf = 0.0684 , 95.0 % confidence interval :[0.0415,0.0952]
+Number of function calls = 512 , Pf = 0.0859 , 95.0 % confidence interval :[0.0565,0.1154]
+Number of function calls = 512 , Pf = 0.0938 , 95.0 % confidence interval :[0.0628,0.1247]
+Number of function calls = 512 , Pf = 0.0566 , 95.0 % confidence interval :[0.0322,0.0811]
+Number of function calls = 1024 , Pf = 0.0713 , 95.0 % confidence interval :[0.0519,0.0906]
+Number of function calls = 1024 , Pf = 0.0791 , 95.0 % confidence interval :[0.0588,0.0994]
+Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0638,0.1061]
+Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0640,0.1059]
+Number of function calls = 1024 , Pf = 0.0830 , 95.0 % confidence interval :[0.0625,0.1035]
+Number of function calls = 1024 , Pf = 0.0869 , 95.0 % confidence interval :[0.0658,0.1080]
+Number of function calls = 1024 , Pf = 0.0869 , 95.0 % confidence interval :[0.0657,0.1081]
+Number of function calls = 1024 , Pf = 0.0771 , 95.0 % confidence interval :[0.0570,0.0973]
+Number of function calls = 1024 , Pf = 0.0830 , 95.0 % confidence interval :[0.0622,0.1038]
 Number of function calls = 1024 , Pf = 0.0742 , 95.0 % confidence interval :[0.0546,0.0939]
-Number of function calls = 1024 , Pf = 0.0889 , 95.0 % confidence interval :[0.0675,0.1102]
-Number of function calls = 1024 , Pf = 0.0674 , 95.0 % confidence interval :[0.0485,0.0862]
-Number of function calls = 1024 , Pf = 0.0850 , 95.0 % confidence interval :[0.0642,0.1058]
-Number of function calls = 1024 , Pf = 0.0635 , 95.0 % confidence interval :[0.0456,0.0814]
-Number of function calls = 1024 , Pf = 0.0781 , 95.0 % confidence interval :[0.0580,0.0983]
-Number of function calls = 1024 , Pf = 0.0967 , 95.0 % confidence interval :[0.0744,0.1189]
-Number of function calls = 1024 , Pf = 0.0820 , 95.0 % confidence interval :[0.0615,0.1026]
-Number of function calls = 1024 , Pf = 0.0752 , 95.0 % confidence interval :[0.0553,0.0951]
-Number of function calls = 2048 , Pf = 0.0786 , 95.0 % confidence interval :[0.0644,0.0929]
-Number of function calls = 2048 , Pf = 0.0776 , 95.0 % confidence interval :[0.0636,0.0917]
-Number of function calls = 2048 , Pf = 0.0840 , 95.0 % confidence interval :[0.0692,0.0988]
-Number of function calls = 2048 , Pf = 0.0664 , 95.0 % confidence interval :[0.0531,0.0797]
+Number of function calls = 2048 , Pf = 0.0840 , 95.0 % confidence interval :[0.0693,0.0987]
+Number of function calls = 2048 , Pf = 0.0864 , 95.0 % confidence interval :[0.0716,0.1013]
+Number of function calls = 2048 , Pf = 0.0894 , 95.0 % confidence interval :[0.0743,0.1044]
+Number of function calls = 2048 , Pf = 0.0864 , 95.0 % confidence interval :[0.0715,0.1014]
+Number of function calls = 2048 , Pf = 0.0757 , 95.0 % confidence interval :[0.0618,0.0896]
+Number of function calls = 2048 , Pf = 0.0781 , 95.0 % confidence interval :[0.0638,0.0925]
 Number of function calls = 2048 , Pf = 0.0786 , 95.0 % confidence interval :[0.0643,0.0930]
-Number of function calls = 2048 , Pf = 0.0742 , 95.0 % confidence interval :[0.0605,0.0880]
-Number of function calls = 2048 , Pf = 0.0791 , 95.0 % confidence interval :[0.0647,0.0935]
-Number of function calls = 2048 , Pf = 0.0815 , 95.0 % confidence interval :[0.0670,0.0961]
-Number of function calls = 2048 , Pf = 0.0767 , 95.0 % confidence interval :[0.0626,0.0907]
-Number of function calls = 2048 , Pf = 0.0801 , 95.0 % confidence interval :[0.0658,0.0944]
-Number of function calls = 4096 , Pf = 0.0820 , 95.0 % confidence interval :[0.0717,0.0923]
-Number of function calls = 4096 , Pf = 0.0789 , 95.0 % confidence interval :[0.0688,0.0889]
-Number of function calls = 4096 , Pf = 0.0754 , 95.0 % confidence interval :[0.0656,0.0853]
-Number of function calls = 4096 , Pf = 0.0806 , 95.0 % confidence interval :[0.0703,0.0908]
-Number of function calls = 4096 , Pf = 0.0833 , 95.0 % confidence interval :[0.0728,0.0937]
-Number of function calls = 4096 , Pf = 0.0786 , 95.0 % confidence interval :[0.0686,0.0887]
-Number of function calls = 4096 , Pf = 0.0720 , 95.0 % confidence interval :[0.0623,0.0817]
-Number of function calls = 4096 , Pf = 0.0723 , 95.0 % confidence interval :[0.0625,0.0820]
-Number of function calls = 4096 , Pf = 0.0754 , 95.0 % confidence interval :[0.0656,0.0853]
-Number of function calls = 4096 , Pf = 0.0732 , 95.0 % confidence interval :[0.0635,0.0830]
-Number of function calls = 8192 , Pf = 0.0786 , 95.0 % confidence interval :[0.0715,0.0858]
+Number of function calls = 2048 , Pf = 0.0747 , 95.0 % confidence interval :[0.0609,0.0885]
+Number of function calls = 2048 , Pf = 0.0674 , 95.0 % confidence interval :[0.0542,0.0806]
+Number of function calls = 2048 , Pf = 0.0796 , 95.0 % confidence interval :[0.0653,0.0938]
+Number of function calls = 4096 , Pf = 0.0735 , 95.0 % confidence interval :[0.0637,0.0833]
+Number of function calls = 4096 , Pf = 0.0774 , 95.0 % confidence interval :[0.0674,0.0874]
+Number of function calls = 4096 , Pf = 0.0825 , 95.0 % confidence interval :[0.0722,0.0929]
+Number of function calls = 4096 , Pf = 0.0764 , 95.0 % confidence interval :[0.0665,0.0863]
+Number of function calls = 4096 , Pf = 0.0815 , 95.0 % confidence interval :[0.0713,0.0918]
+Number of function calls = 4096 , Pf = 0.0791 , 95.0 % confidence interval :[0.0689,0.0893]
+Number of function calls = 4096 , Pf = 0.0784 , 95.0 % confidence interval :[0.0682,0.0885]
+Number of function calls = 4096 , Pf = 0.0740 , 95.0 % confidence interval :[0.0641,0.0838]
+Number of function calls = 4096 , Pf = 0.0747 , 95.0 % confidence interval :[0.0649,0.0845]
+Number of function calls = 4096 , Pf = 0.0815 , 95.0 % confidence interval :[0.0712,0.0918]
+Number of function calls = 8192 , Pf = 0.0782 , 95.0 % confidence interval :[0.0711,0.0854]
+Number of function calls = 8192 , Pf = 0.0781 , 95.0 % confidence interval :[0.0710,0.0852]
+Number of function calls = 8192 , Pf = 0.0770 , 95.0 % confidence interval :[0.0700,0.0841]
+Number of function calls = 8192 , Pf = 0.0768 , 95.0 % confidence interval :[0.0697,0.0838]
+Number of function calls = 8192 , Pf = 0.0745 , 95.0 % confidence interval :[0.0675,0.0815]
+Number of function calls = 8192 , Pf = 0.0780 , 95.0 % confidence interval :[0.0709,0.0851]
+Number of function calls = 8192 , Pf = 0.0797 , 95.0 % confidence interval :[0.0725,0.0869]
 Number of function calls = 8192 , Pf = 0.0797 , 95.0 % confidence interval :[0.0725,0.0869]
-Number of function calls = 8192 , Pf = 0.0785 , 95.0 % confidence interval :[0.0713,0.0856]
-Number of function calls = 8192 , Pf = 0.0809 , 95.0 % confidence interval :[0.0737,0.0882]
-Number of function calls = 8192 , Pf = 0.0785 , 95.0 % confidence interval :[0.0714,0.0856]
-Number of function calls = 8192 , Pf = 0.0764 , 95.0 % confidence interval :[0.0694,0.0834]
-Number of function calls = 8192 , Pf = 0.0833 , 95.0 % confidence interval :[0.0759,0.0906]
-Number of function calls = 8192 , Pf = 0.0803 , 95.0 % confidence interval :[0.0731,0.0875]
-Number of function calls = 8192 , Pf = 0.0789 , 95.0 % confidence interval :[0.0717,0.0860]
-Number of function calls = 8192 , Pf = 0.0859 , 95.0 % confidence interval :[0.0785,0.0934]
-Number of function calls = 16384 , Pf = 0.0776 , 95.0 % confidence interval :[0.0726,0.0827]
-Number of function calls = 16384 , Pf = 0.0801 , 95.0 % confidence interval :[0.0750,0.0852]
-Number of function calls = 16384 , Pf = 0.0792 , 95.0 % confidence interval :[0.0741,0.0842]
-Number of function calls = 16384 , Pf = 0.0768 , 95.0 % confidence interval :[0.0718,0.0818]
-Number of function calls = 16384 , Pf = 0.0770 , 95.0 % confidence interval :[0.0720,0.0820]
-Number of function calls = 16384 , Pf = 0.0778 , 95.0 % confidence interval :[0.0728,0.0829]
-Number of function calls = 16384 , Pf = 0.0804 , 95.0 % confidence interval :[0.0753,0.0855]
-Number of function calls = 16384 , Pf = 0.0817 , 95.0 % confidence interval :[0.0765,0.0868]
-Number of function calls = 16384 , Pf = 0.0779 , 95.0 % confidence interval :[0.0728,0.0829]
-Number of function calls = 16384 , Pf = 0.0802 , 95.0 % confidence interval :[0.0751,0.0853]
-Number of function calls = 32768 , Pf = 0.0785 , 95.0 % confidence interval :[0.0749,0.0821]
-Number of function calls = 32768 , Pf = 0.0782 , 95.0 % confidence interval :[0.0746,0.0818]
-Number of function calls = 32768 , Pf = 0.0803 , 95.0 % confidence interval :[0.0767,0.0839]
-Number of function calls = 32768 , Pf = 0.0782 , 95.0 % confidence interval :[0.0747,0.0818]
+Number of function calls = 8192 , Pf = 0.0760 , 95.0 % confidence interval :[0.0690,0.0831]
+Number of function calls = 8192 , Pf = 0.0818 , 95.0 % confidence interval :[0.0745,0.0891]
+Number of function calls = 16384 , Pf = 0.0825 , 95.0 % confidence interval :[0.0773,0.0876]
+Number of function calls = 16384 , Pf = 0.0812 , 95.0 % confidence interval :[0.0761,0.0863]
+Number of function calls = 16384 , Pf = 0.0760 , 95.0 % confidence interval :[0.0710,0.0810]
+Number of function calls = 16384 , Pf = 0.0790 , 95.0 % confidence interval :[0.0740,0.0841]
+Number of function calls = 16384 , Pf = 0.0793 , 95.0 % confidence interval :[0.0743,0.0844]
+Number of function calls = 16384 , Pf = 0.0806 , 95.0 % confidence interval :[0.0755,0.0857]
+Number of function calls = 16384 , Pf = 0.0785 , 95.0 % confidence interval :[0.0735,0.0835]
+Number of function calls = 16384 , Pf = 0.0779 , 95.0 % confidence interval :[0.0729,0.0830]
+Number of function calls = 16384 , Pf = 0.0786 , 95.0 % confidence interval :[0.0736,0.0837]
+Number of function calls = 16384 , Pf = 0.0750 , 95.0 % confidence interval :[0.0701,0.0799]
+Number of function calls = 32768 , Pf = 0.0772 , 95.0 % confidence interval :[0.0737,0.0808]
+Number of function calls = 32768 , Pf = 0.0766 , 95.0 % confidence interval :[0.0731,0.0801]
+Number of function calls = 32768 , Pf = 0.0796 , 95.0 % confidence interval :[0.0760,0.0832]
 Number of function calls = 32768 , Pf = 0.0802 , 95.0 % confidence interval :[0.0766,0.0838]
-Number of function calls = 32768 , Pf = 0.0775 , 95.0 % confidence interval :[0.0740,0.0811]
-Number of function calls = 32768 , Pf = 0.0794 , 95.0 % confidence interval :[0.0758,0.0830]
-Number of function calls = 32768 , Pf = 0.0794 , 95.0 % confidence interval :[0.0758,0.0830]
-Number of function calls = 32768 , Pf = 0.0799 , 95.0 % confidence interval :[0.0763,0.0835]
-Number of function calls = 32768 , Pf = 0.0789 , 95.0 % confidence interval :[0.0753,0.0825]
-Number of function calls = 65536 , Pf = 0.0781 , 95.0 % confidence interval :[0.0756,0.0806]
-Number of function calls = 65536 , Pf = 0.0777 , 95.0 % confidence interval :[0.0752,0.0802]
-Number of function calls = 65536 , Pf = 0.0804 , 95.0 % confidence interval :[0.0779,0.0830]
-Number of function calls = 65536 , Pf = 0.0778 , 95.0 % confidence interval :[0.0753,0.0803]
+Number of function calls = 32768 , Pf = 0.0796 , 95.0 % confidence interval :[0.0760,0.0831]
+Number of function calls = 32768 , Pf = 0.0772 , 95.0 % confidence interval :[0.0737,0.0807]
+Number of function calls = 32768 , Pf = 0.0786 , 95.0 % confidence interval :[0.0750,0.0822]
+Number of function calls = 32768 , Pf = 0.0778 , 95.0 % confidence interval :[0.0743,0.0814]
+Number of function calls = 32768 , Pf = 0.0810 , 95.0 % confidence interval :[0.0774,0.0846]
+Number of function calls = 32768 , Pf = 0.0808 , 95.0 % confidence interval :[0.0772,0.0844]
+Number of function calls = 65536 , Pf = 0.0790 , 95.0 % confidence interval :[0.0765,0.0815]
+Number of function calls = 65536 , Pf = 0.0774 , 95.0 % confidence interval :[0.0749,0.0799]
 Number of function calls = 65536 , Pf = 0.0786 , 95.0 % confidence interval :[0.0761,0.0812]
-Number of function calls = 65536 , Pf = 0.0779 , 95.0 % confidence interval :[0.0754,0.0804]
-Number of function calls = 65536 , Pf = 0.0818 , 95.0 % confidence interval :[0.0792,0.0844]
-Number of function calls = 65536 , Pf = 0.0793 , 95.0 % confidence interval :[0.0768,0.0819]
-Number of function calls = 65536 , Pf = 0.0786 , 95.0 % confidence interval :[0.0761,0.0811]
-Number of function calls = 65536 , Pf = 0.0814 , 95.0 % confidence interval :[0.0788,0.0839]
+Number of function calls = 65536 , Pf = 0.0781 , 95.0 % confidence interval :[0.0756,0.0807]
+Number of function calls = 65536 , Pf = 0.0793 , 95.0 % confidence interval :[0.0767,0.0818]
+Number of function calls = 65536 , Pf = 0.0793 , 95.0 % confidence interval :[0.0768,0.0818]
+Number of function calls = 65536 , Pf = 0.0806 , 95.0 % confidence interval :[0.0781,0.0832]
+Number of function calls = 65536 , Pf = 0.0784 , 95.0 % confidence interval :[0.0759,0.0810]
+Number of function calls = 65536 , Pf = 0.0783 , 95.0 % confidence interval :[0.0758,0.0808]
+Number of function calls = 65536 , Pf = 0.0789 , 95.0 % confidence interval :[0.0763,0.0814]
 </pre></div>
 </div>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">elapsedTime</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">startTime</span>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Elapsed = </span><span class="si">%.2f</span><span class="s2"> (s)&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">elapsedTime</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Elapsed = 1.75 (s)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Elapsed = 1.74 (s)
 </pre></div>
 </div>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sampleSizeArray</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">)]</span>
@@ -389,7 +386,7 @@ <h3 id="searchlabel">Quick search</h3>
 <img src="../_images/sphx_glr_plot_convergence_reliability_mc_001.png" srcset="../_images/sphx_glr_plot_convergence_reliability_mc_001.png" alt="Convergence of Monte-Carlo method - problem = R-S" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.174 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.177 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-convergence-reliability-mc-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/2c4b93614fe36215c71cfe87dbdc9164/plot_convergence_reliability_mc.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_convergence_reliability_mc.ipynb</span></code></a></p>
diff --git a/otbenchmark/master/auto_examples/plot_crosscut_distribution_2d.html b/otbenchmark/master/auto_examples/plot_crosscut_distribution_2d.html
index fab682147c0..38ad843d3e6 100644
--- a/otbenchmark/master/auto_examples/plot_crosscut_distribution_2d.html
+++ b/otbenchmark/master/auto_examples/plot_crosscut_distribution_2d.html
@@ -73,9 +73,6 @@ <h3><a href="../index.html">Table of Contents</a></h3>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
@@ -157,7 +154,7 @@ <h2>Plot cross-cuts of the distribution<a class="headerlink" href="#plot-cross-c
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">crossCut</span><span class="o">.</span><span class="n">drawMarginalPDF</span><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_crosscut_distribution_2d_002.png" srcset="../_images/sphx_glr_plot_crosscut_distribution_2d_002.png" alt="Iso-values of marginal PDF" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 3.997 seconds)</p>
+<img src="../_images/sphx_glr_plot_crosscut_distribution_2d_002.png" srcset="../_images/sphx_glr_plot_crosscut_distribution_2d_002.png" alt="Iso-values of marginal PDF" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.066 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-crosscut-distribution-2d-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/7504592acb2bebb9a215919dc53f7e9d/plot_crosscut_distribution_2d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_crosscut_distribution_2d.ipynb</span></code></a></p>
diff --git a/otbenchmark/master/auto_examples/plot_crosscut_distribution_3d.html b/otbenchmark/master/auto_examples/plot_crosscut_distribution_3d.html
index 617cef636d3..56b54982d95 100644
--- a/otbenchmark/master/auto_examples/plot_crosscut_distribution_3d.html
+++ b/otbenchmark/master/auto_examples/plot_crosscut_distribution_3d.html
@@ -73,9 +73,6 @@ <h3><a href="../index.html">Table of Contents</a></h3>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
@@ -164,7 +161,7 @@ <h2>Print the iso-values of the distribution<a class="headerlink" href="#print-t
 <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_crosscut_distribution_3d_002.png" srcset="../_images/sphx_glr_plot_crosscut_distribution_3d_002.png" alt="Iso-values of marginal PDF" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.366 seconds)</p>
+<img src="../_images/sphx_glr_plot_crosscut_distribution_3d_002.png" srcset="../_images/sphx_glr_plot_crosscut_distribution_3d_002.png" alt="Iso-values of marginal PDF" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.724 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-crosscut-distribution-3d-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/059115d02a2ce05d88aa60eb327fb5a9/plot_crosscut_distribution_3d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_crosscut_distribution_3d.ipynb</span></code></a></p>
diff --git a/otbenchmark/master/auto_examples/plot_methodFactory.html b/otbenchmark/master/auto_examples/plot_methodFactory.html
index 9bcb410d916..120e3d592da 100644
--- a/otbenchmark/master/auto_examples/plot_methodFactory.html
+++ b/otbenchmark/master/auto_examples/plot_methodFactory.html
@@ -73,9 +73,6 @@ <h3><a href="../index.html">Table of Contents</a></h3>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
@@ -238,7 +235,7 @@ <h2>Create a LHS algorithm<a class="headerlink" href="#create-a-lhs-algorithm" t
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.0
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.439 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.465 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-methodfactory-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/cb2e4c0a435798c98fccd4b5b9e51588/plot_methodFactory.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_methodFactory.ipynb</span></code></a></p>
diff --git a/otbenchmark/master/auto_examples/sensitivity_problems/index.html b/otbenchmark/master/auto_examples/sensitivity_problems/index.html
index 9ce72e636eb..da97625e6fa 100644
--- a/otbenchmark/master/auto_examples/sensitivity_problems/index.html
+++ b/otbenchmark/master/auto_examples/sensitivity_problems/index.html
@@ -68,9 +68,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Sensitivity examples</a></li>
 </ul>
 </li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
@@ -113,9 +110,45 @@ <h3 id="searchlabel">Quick search</h3>
 <div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Print the sensitivity analysis problems"><img alt="" src="../../_images/sphx_glr_plot_print_problems_thumb.png" />
 <p><a class="reference internal" href="plot_print_problems.html#sphx-glr-auto-examples-sensitivity-problems-plot-print-problems-py"><span class="std std-ref">Print the sensitivity analysis problems</span></a></p>
   <div class="sphx-glr-thumbnail-title">Print the sensitivity analysis problems</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Flooding test function"><img alt="" src="../../_images/sphx_glr_plot_flood_sensitivity_thumb.png" />
+<p><a class="reference internal" href="plot_flood_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-flood-sensitivity-py"><span class="std std-ref">Benchmark the Flooding test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Flooding test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Borgonovo test function"><img alt="" src="../../_images/sphx_glr_plot_borgonovo_sensitivity_thumb.png" />
+<p><a class="reference internal" href="plot_borgonovo_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-borgonovo-sensitivity-py"><span class="std std-ref">Benchmark the Borgonovo test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Borgonovo test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Ishigami test function"><img alt="" src="../../_images/sphx_glr_plot_ishigami_sensitivity_thumb.png" />
+<p><a class="reference internal" href="plot_ishigami_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-ishigami-sensitivity-py"><span class="std std-ref">Benchmark the Ishigami test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Ishigami test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Dirichlet test function"><img alt="" src="../../_images/sphx_glr_plot_dirichlet_sensitivity_thumb.png" />
+<p><a class="reference internal" href="plot_dirichlet_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-dirichlet-sensitivity-py"><span class="std std-ref">Benchmark the Dirichlet test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Dirichlet test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Borehole test function"><img alt="" src="../../_images/sphx_glr_plot_borehole_sensitivity_thumb.png" />
+<p><a class="reference internal" href="plot_borehole_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-borehole-sensitivity-py"><span class="std std-ref">Benchmark the Borehole test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Borehole test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the NLOscillator test function"><img alt="" src="../../_images/sphx_glr_plot_nloscillator_sensitivity_thumb.png" />
+<p><a class="reference internal" href="plot_nloscillator_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-nloscillator-sensitivity-py"><span class="std std-ref">Benchmark the NLOscillator test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the NLOscillator test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the gaussian sum test function"><img alt="" src="../../_images/sphx_glr_plot_gaussian_sum_thumb.png" />
+<p><a class="reference internal" href="plot_gaussian_sum.html#sphx-glr-auto-examples-sensitivity-problems-plot-gaussian-sum-py"><span class="std std-ref">Benchmark the gaussian sum test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the gaussian sum test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the gaussian product test function"><img alt="" src="../../_images/sphx_glr_plot_gaussian_product_sensitivity_thumb.png" />
+<p><a class="reference internal" href="plot_gaussian_product_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-gaussian-product-sensitivity-py"><span class="std std-ref">Benchmark the gaussian product test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the gaussian product test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Morris test function"><img alt="" src="../../_images/sphx_glr_plot_morris_sensitivity_thumb.png" />
+<p><a class="reference internal" href="plot_morris_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-morris-sensitivity-py"><span class="std std-ref">Benchmark the Morris test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Morris test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the Oakley-O&#x27;Hagan test function"><img alt="" src="../../_images/sphx_glr_plot_oakleyohagan_sensitivity_thumb.png" />
+<p><a class="reference internal" href="plot_oakleyohagan_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-oakleyohagan-sensitivity-py"><span class="std std-ref">Benchmark the Oakley-O’Hagan test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the Oakley-O'Hagan test function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark the G-Sobol test function"><img alt="" src="../../_images/sphx_glr_plot_gsobol_sensitivity_thumb.png" />
+<p><a class="reference internal" href="plot_gsobol_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-gsobol-sensitivity-py"><span class="std std-ref">Benchmark the G-Sobol test function</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark the G-Sobol test function</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Convergence of estimators on Ishigami"><img alt="" src="../../_images/sphx_glr_plot_convergence_ishigami_thumb.png" />
 <p><a class="reference internal" href="plot_convergence_ishigami.html#sphx-glr-auto-examples-sensitivity-problems-plot-convergence-ishigami-py"><span class="std std-ref">Convergence of estimators on Ishigami</span></a></p>
   <div class="sphx-glr-thumbnail-title">Convergence of estimators on Ishigami</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Benchmark sensitivity analysis methods"><img alt="" src="../../_images/sphx_glr_plot_benchmark_sensitivity_methods_thumb.png" />
+<p><a class="reference internal" href="plot_benchmark_sensitivity_methods.html#sphx-glr-auto-examples-sensitivity-problems-plot-benchmark-sensitivity-methods-py"><span class="std std-ref">Benchmark sensitivity analysis methods</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmark sensitivity analysis methods</div>
 </div></div><div class="toctree-wrapper compound">
 </div>
 </section>
diff --git a/otbenchmark/master/auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.html b/otbenchmark/master/auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.html
new file mode 100644
index 00000000000..3c78301412e
--- /dev/null
+++ b/otbenchmark/master/auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.html
@@ -0,0 +1,402 @@
+<!DOCTYPE html>
+
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+    <h1>
+      <img src="../../_static/logo-openturns-wo-bg.png" alt="" width=100px height=100px />
+      otbenchmark
+    </h1>
+    <h2> Module otbenchmark </h2>
+  </a>
+</div>
+
+    <div class="related" role="navigation" aria-label="Related">
+      <h3>Navigation</h3>
+      <ul>
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+        <li class="nav-item nav-item-0"><a href="../../index.html">otbenchmark 0.1 documentation</a> &#187;</li>
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+        <li class="nav-item nav-item-this"><a href="">Benchmark sensitivity analysis methods</a></li> 
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+      <div class="sphinxsidebar" role="navigation" aria-label="Main">
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+<h3><a href="../../index.html">Table of Contents</a></h3>
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+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html#sensitivity-examples">Sensitivity examples</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="index.html">Sensitivity examples</a></li>
+</ul>
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+
+  <div>
+    <h4>Previous topic</h4>
+    <p class="topless"><a href="plot_convergence_ishigami.html"
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+    <form class="search" action="../../search.html" method="get">
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-sensitivity-problems-plot-benchmark-sensitivity-methods-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-sensitivity-analysis-methods">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-benchmark-sensitivity-methods-py"></span><h1>Benchmark sensitivity analysis methods<a class="headerlink" href="#benchmark-sensitivity-analysis-methods" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+</pre></div>
+</div>
+<p>When we estimate Sobol’ indices, we may encounter the following warning messages:
+<code class="docutils literal notranslate"><span class="pre">`</span>
+<span class="pre">WRN</span> <span class="pre">-</span> <span class="pre">The</span> <span class="pre">estimated</span> <span class="pre">first</span> <span class="pre">order</span> <span class="pre">Sobol</span> <span class="pre">index</span> <span class="pre">(2)</span> <span class="pre">is</span> <span class="pre">greater</span> <span class="pre">than</span> <span class="pre">its</span> <span class="pre">total</span> <span class="pre">order</span> <span class="pre">index</span> <span class="pre">...</span>
+<span class="pre">WRN</span> <span class="pre">-</span> <span class="pre">The</span> <span class="pre">estimated</span> <span class="pre">total</span> <span class="pre">order</span> <span class="pre">Sobol</span> <span class="pre">index</span> <span class="pre">(2)</span> <span class="pre">is</span> <span class="pre">lesser</span> <span class="pre">than</span> <span class="pre">first</span> <span class="pre">order</span> <span class="pre">index</span> <span class="pre">...</span>
+<span class="pre">`</span></code>
+Lots of these messages are printed in the current Notebook. This is why we disable them with:</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">Log</span><span class="o">.</span><span class="n">Show</span><span class="p">(</span><span class="n">ot</span><span class="o">.</span><span class="n">Log</span><span class="o">.</span><span class="n">NONE</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Use Borgonovo problem</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">BorgonovoSensitivity</span><span class="p">()</span>
+<span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<section id="saltelli-estimator-with-monte-carlo-sample">
+<h2>Saltelli estimator with Monte-Carlo sample<a class="headerlink" href="#saltelli-estimator-with-monte-carlo-sample" title="Link to this heading">¶</a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sample_size</span> <span class="o">=</span> <span class="mi">10000</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span>
+    <span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">sample_size</span>
+<span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  10000
+Computed first order =  [0.18553,0.173199,0.637105]
+Exact first order =  [0.157895,0.157895,0.631579]
+Computed total order =  [0.203344,0.210474,0.620406]
+Exact total order =  [0.210526,0.210526,0.631579]
+</pre></div>
+</div>
+</section>
+<section id="saltelli-estimator-with-quasi-monte-carlo-sample">
+<h2>Saltelli estimator with Quasi Monte-Carlo sample<a class="headerlink" href="#saltelli-estimator-with-quasi-monte-carlo-sample" title="Link to this heading">¶</a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sample_size</span> <span class="o">=</span> <span class="mi">500</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">sequence</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolSequence</span><span class="p">(</span><span class="n">dimension</span><span class="p">)</span>
+<span class="n">restart</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">experiment</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">LowDiscrepancyExperiment</span><span class="p">(</span><span class="n">sequence</span><span class="p">,</span> <span class="n">distribution</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">,</span> <span class="n">restart</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">experiment</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span>
+    <span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">sample_size</span>
+<span class="p">)</span>
+<span class="n">first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  500
+Computed first order =  [0.18553,0.173199,0.637105]
+Exact first order =  [0.157895,0.157895,0.631579]
+Computed total order =  [0.203344,0.210474,0.620406]
+Exact total order =  [0.210526,0.210526,0.631579]
+</pre></div>
+</div>
+</section>
+<section id="loop-over-the-estimators">
+<h2>Loop over the estimators<a class="headerlink" href="#loop-over-the-estimators" title="Link to this heading">¶</a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Available estimators:&quot;</span><span class="p">)</span>
+<span class="n">estimators_list</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">SensitivityBenchmarkMetaAlgorithm</span><span class="o">.</span><span class="n">GetEstimators</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">sobolAlgorithm</span> <span class="ow">in</span> <span class="n">estimators_list</span><span class="p">:</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="n">sobolAlgorithm</span><span class="o">.</span><span class="n">getClassName</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot; - &quot;</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Available estimators:
+ -  SaltelliSensitivityAlgorithm
+ -  MartinezSensitivityAlgorithm
+ -  JansenSensitivityAlgorithm
+ -  MauntzKucherenkoSensitivityAlgorithm
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">metaSAAlgorithm</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">SensitivityBenchmarkMetaAlgorithm</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Monte-Carlo sampling&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">sobolAlgorithm</span> <span class="ow">in</span> <span class="n">estimators_list</span><span class="p">:</span>
+    <span class="p">(</span>
+        <span class="n">computed_first_order</span><span class="p">,</span>
+        <span class="n">computed_total_order</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">metaSAAlgorithm</span><span class="o">.</span><span class="n">runSamplingEstimator</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="n">sobolAlgorithm</span><span class="o">.</span><span class="n">getClassName</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    S = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    T = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Monte-Carlo sampling
+SaltelliSensitivityAlgorithm
+    S =  [0.199997,0.180685,0.664071]
+    T =  [0.223721,0.228732,0.6502]
+MartinezSensitivityAlgorithm
+    S =  [0.191903,0.173622,0.638601]
+    T =  [0.2242,0.207898,0.591462]
+JansenSensitivityAlgorithm
+    S =  [0.0952801,0.125079,0.615803]
+    T =  [0.253686,0.237742,0.592254]
+MauntzKucherenkoSensitivityAlgorithm
+    S =  [0.205369,0.314775,0.698245]
+    T =  [0.220371,0.172107,0.567821]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Quasi Monte-Carlo sampling&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">estimator</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;Saltelli&quot;</span><span class="p">,</span> <span class="s2">&quot;Martinez&quot;</span><span class="p">,</span> <span class="s2">&quot;Jansen&quot;</span><span class="p">,</span> <span class="s2">&quot;MauntzKucherenko&quot;</span><span class="p">]:</span>
+    <span class="p">(</span>
+        <span class="n">computed_first_order</span><span class="p">,</span>
+        <span class="n">computed_total_order</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">metaSAAlgorithm</span><span class="o">.</span><span class="n">runSamplingEstimator</span><span class="p">(</span>
+        <span class="n">sample_size</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="n">estimator</span><span class="p">,</span> <span class="n">sampling_method</span><span class="o">=</span><span class="s2">&quot;QMC&quot;</span>
+    <span class="p">)</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="n">sobolAlgorithm</span><span class="o">.</span><span class="n">getClassName</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    S = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    T = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Quasi Monte-Carlo sampling
+MauntzKucherenkoSensitivityAlgorithm
+    S =  [0.147144,0.151326,0.624455]
+    T =  [0.212622,0.209087,0.643622]
+MauntzKucherenkoSensitivityAlgorithm
+    S =  [0.147601,0.150976,0.629051]
+    T =  [0.210037,0.210799,0.640943]
+MauntzKucherenkoSensitivityAlgorithm
+    S =  [0.151099,0.149846,0.632098]
+    T =  [0.20915,0.21105,0.635553]
+MauntzKucherenkoSensitivityAlgorithm
+    S =  [0.159027,0.163209,0.636338]
+    T =  [0.212622,0.209087,0.643622]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Polynomial chaos&quot;</span><span class="p">)</span>
+<span class="n">sample_size</span> <span class="o">=</span> <span class="mi">500</span>
+<span class="p">(</span>
+    <span class="n">computed_first_order</span><span class="p">,</span>
+    <span class="n">computed_total_order</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">=</span> <span class="n">metaSAAlgorithm</span><span class="o">.</span><span class="n">runPolynomialChaosEstimator</span><span class="p">(</span>
+    <span class="n">sample_size_train</span><span class="o">=</span><span class="n">sample_size</span><span class="p">,</span>
+    <span class="n">sample_size_test</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">total_degree</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">hyperbolic_quasinorm</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    S = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;    T = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Polynomial chaos
+    S =  [0.157895,0.157895,0.631579]
+    T =  [0.210526,0.210526,0.631579]
+</pre></div>
+</div>
+</section>
+<section id="define-the-metric">
+<h2>Define the metric<a class="headerlink" href="#define-the-metric" title="Link to this heading">¶</a></h2>
+<p>We consider the following accuracy metrics:
+* the vector or log relative errors for a given index (first order or total order),
+* the mean log relative error, as the mean of the LRE vector (first order or total order),
+* the average mean log relative error, as the mean of the first and total order mean log relative error.</p>
+<p>Larger LRE values are prefered.</p>
+<p>The first order (resp. total order) mean LRE represents the mean number of digits for all components
+of the first order indices (resp. total order indices).
+The average mean LRE represents the mean LRE for both first and total order indices.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">S_LRE</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Point</span><span class="p">(</span><span class="n">dimension</span><span class="p">)</span>
+<span class="n">T_LRE</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Point</span><span class="p">(</span><span class="n">dimension</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimension</span><span class="p">):</span>
+    <span class="n">S_LRE</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">ComputeLogRelativeError</span><span class="p">(</span>
+        <span class="n">computed_first_order</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">exact_first_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="p">)</span>
+    <span class="n">T_LRE</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">ComputeLogRelativeError</span><span class="p">(</span>
+        <span class="n">computed_total_order</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">exact_total_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;LRE S = &quot;</span><span class="p">,</span> <span class="n">S_LRE</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;LRE T = &quot;</span><span class="p">,</span> <span class="n">T_LRE</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>LRE S =  [14.7136,14.5509,15.153]
+LRE T =  [15.0349,14.8008,15.153]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mean_LRE_S</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">S_LRE</span><span class="p">)</span> <span class="o">/</span> <span class="n">dimension</span>
+<span class="n">mean_LRE_T</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">T_LRE</span><span class="p">)</span> <span class="o">/</span> <span class="n">dimension</span>
+<span class="n">mean_LRE</span> <span class="o">=</span> <span class="p">(</span><span class="n">mean_LRE_S</span> <span class="o">+</span> <span class="n">mean_LRE_T</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean LRE S = </span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mean_LRE_S</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean LRE T = </span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mean_LRE_T</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean LRE = </span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mean_LRE</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Mean LRE S = 14.81
+Mean LRE T = 15.00
+Mean LRE = 14.90
+</pre></div>
+</div>
+<p>The digit per point ratio measure the number of digits relatively to the sample size. A greater value is prefered.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">digit_per_point_ratio</span> <span class="o">=</span> <span class="n">mean_LRE</span> <span class="o">/</span> <span class="n">sample_size</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Digit / point = </span><span class="si">%.3e</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">digit_per_point_ratio</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Digit / point = 2.980e-02
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.034 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-benchmark-sensitivity-methods-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/13b9169f4ad20d57745bd3f8f0cb6282/plot_benchmark_sensitivity_methods.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_benchmark_sensitivity_methods.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/3e62dca12ac3db00aaba5db1a12e3867/plot_benchmark_sensitivity_methods.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_benchmark_sensitivity_methods.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/966aab35daf3bdeee31dc59712af12c7/plot_benchmark_sensitivity_methods.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_benchmark_sensitivity_methods.zip</span></code></a></p>
+</div>
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diff --git a/otbenchmark/master/auto_examples/sensitivity_problems/plot_borehole_sensitivity.html b/otbenchmark/master/auto_examples/sensitivity_problems/plot_borehole_sensitivity.html
new file mode 100644
index 00000000000..349fa4460dd
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+          <li class="nav-item nav-item-2"><a href="index.html" accesskey="U">Sensitivity examples</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">Benchmark the Borehole test function</a></li> 
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+    <p class="topless"><a href="plot_dirichlet_sensitivity.html"
+                          title="previous chapter">Benchmark the Dirichlet test function</a></p>
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+    <h4>Next topic</h4>
+    <p class="topless"><a href="plot_nloscillator_sensitivity.html"
+                          title="next chapter">Benchmark the NLOscillator test function</a></p>
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-sensitivity-problems-plot-borehole-sensitivity-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-borehole-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-borehole-sensitivity-py"></span><h1>Benchmark the Borehole test function<a class="headerlink" href="#benchmark-the-borehole-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">BoreholeSensitivity</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = Borehole
+distribution = ComposedDistribution(Normal(mu = 0.1, sigma = 0.0161812), LogNormal(muLog = 7.71, sigmaLog = 1.0056, gamma = 0), Uniform(a = 63070, b = 115600), Uniform(a = 990, b = 1110), Uniform(a = 63.1, b = 116), Uniform(a = 700, b = 820), Uniform(a = 1120, b = 1680), Uniform(a = 9855, b = 12045), IndependentCopula(dimension = 8))
+function = [rw,r,Tu,Hu,Tl,Hl,L,Kw]-&gt;[(2*pi_*Tu*(Hu-Hl))/(ln(r/rw)*(1+(2*L*Tu)/(ln(r/rw)*rw^2*Kw)+Tu/Tl))]
+firstOrderIndices = [0.66,0,0,0.09,0,0.09,0.09,0.02]
+totalOrderIndices = [0.69,0,0,0.11,0,0.11,0.1,0.02]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">exact_first_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=8 values=[0.66,0,0,0.09,0,0.09,0.09,0.02]
+</div>
+<br />
+<br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="n">exact_total_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=8 values=[0.69,0,0,0.11,0,0.11,0.1,0.02]
+</div>
+<br />
+<br /><section id="plot-the-function">
+<h2>Plot the function<a class="headerlink" href="#plot-the-function" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">full_sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimension</span><span class="p">)]</span> <span class="o">=</span> <span class="n">inputDesign</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="n">dimension</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span>
+<span class="n">full_description</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">())</span>
+<span class="n">full_description</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">outputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">full_sample</span><span class="o">.</span><span class="n">setDescription</span><span class="p">(</span><span class="n">full_description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">marginal_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">ComposedDistribution</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">full_sample</span><span class="o">.</span><span class="n">getMarginal</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+<span class="n">clouds</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">VisualTest</span><span class="o">.</span><span class="n">DrawPairsMarginals</span><span class="p">(</span><span class="n">full_sample</span><span class="p">,</span> <span class="n">marginal_distribution</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">clouds</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">10.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)})</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_borehole_sensitivity_001.png" srcset="../../_images/sphx_glr_plot_borehole_sensitivity_001.png" alt="plot borehole sensitivity" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_borehole_sensitivity_002.png" srcset="../../_images/sphx_glr_plot_borehole_sensitivity_002.png" alt="plot borehole sensitivity" class = "sphx-glr-single-img"/></section>
+<section id="perform-sensitivity-analysis">
+<h2>Perform sensitivity analysis<a class="headerlink" href="#perform-sensitivity-analysis" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="c1"># Compute absolute error (the LRE cannot be computed,</span>
+<span class="c1"># because S can be zero)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  10000
+Computed first order =  [0.673759,0.00700637,0.00702324,0.0955299,0.00698057,0.106366,0.0916606,0.0283122]
+Exact first order =  [0.66,0,0,0.09,0,0.09,0.09,0.02]
+Computed total order =  [0.699598,-5.69378e-05,-3.20159e-08,0.0973057,9.79799e-05,0.103265,0.095226,0.0247122]
+Exact total order =  [0.69,0,0,0.11,0,0.11,0.1,0.02]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_borehole_sensitivity_003.png" srcset="../../_images/sphx_glr_plot_borehole_sensitivity_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 3.696 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-borehole-sensitivity-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/d0644c1b59c97e301533d2cc27d6f994/plot_borehole_sensitivity.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_borehole_sensitivity.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/6e2038d5b1a24a0d43c32c76904fbca5/plot_borehole_sensitivity.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_borehole_sensitivity.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/6b55e1d416c35886a31041a6892cb0f0/plot_borehole_sensitivity.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_borehole_sensitivity.zip</span></code></a></p>
+</div>
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+
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+        <li class="nav-item nav-item-this"><a href="">Benchmark the Borgonovo test function</a></li> 
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+    <p class="topless"><a href="plot_flood_sensitivity.html"
+                          title="previous chapter">Benchmark the Flooding test function</a></p>
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+    <p class="topless"><a href="plot_ishigami_sensitivity.html"
+                          title="next chapter">Benchmark the Ishigami test function</a></p>
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+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-borgonovo-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-borgonovo-sensitivity-py"></span><h1>Benchmark the Borgonovo test function<a class="headerlink" href="#benchmark-the-borgonovo-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">BorgonovoSensitivity</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = Borgonovo
+distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3))
+function = [x1,x2,x3]-&gt;[x1 * x2 + x3]
+firstOrderIndices = [0.157895,0.157895,0.631579]
+totalOrderIndices = [0.210526,0.210526,0.631579]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">exact_first_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=3 values=[0.157895,0.157895,0.631579]
+</div>
+<br />
+<br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="n">exact_total_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=3 values=[0.210526,0.210526,0.631579]
+</div>
+<br />
+<br /><section id="plot-the-function">
+<h2>Plot the function<a class="headerlink" href="#plot-the-function" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">full_sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimension</span><span class="p">)]</span> <span class="o">=</span> <span class="n">inputDesign</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="n">dimension</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span>
+<span class="n">full_description</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">())</span>
+<span class="n">full_description</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">outputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">full_sample</span><span class="o">.</span><span class="n">setDescription</span><span class="p">(</span><span class="n">full_description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">marginal_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">ComposedDistribution</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">full_sample</span><span class="o">.</span><span class="n">getMarginal</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+<span class="n">clouds</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">VisualTest</span><span class="o">.</span><span class="n">DrawPairsMarginals</span><span class="p">(</span><span class="n">full_sample</span><span class="p">,</span> <span class="n">marginal_distribution</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">clouds</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">)})</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_borgonovo_sensitivity_001.png" srcset="../../_images/sphx_glr_plot_borgonovo_sensitivity_001.png" alt="plot borgonovo sensitivity" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_borgonovo_sensitivity_002.png" srcset="../../_images/sphx_glr_plot_borgonovo_sensitivity_002.png" alt="plot borgonovo sensitivity" class = "sphx-glr-single-img"/></section>
+<section id="perform-sensitivity-analysis">
+<h2>Perform sensitivity analysis<a class="headerlink" href="#perform-sensitivity-analysis" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="c1"># Compute absolute error (the LRE cannot be computed,</span>
+<span class="c1"># because S can be zero)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  10000
+Computed first order =  [0.166136,0.157381,0.623798]
+Exact first order =  [0.157895,0.157895,0.631579]
+Computed total order =  [0.207054,0.2159,0.623765]
+Exact total order =  [0.210526,0.210526,0.631579]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_borgonovo_sensitivity_003.png" srcset="../../_images/sphx_glr_plot_borgonovo_sensitivity_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.145 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-borgonovo-sensitivity-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/018bebf23004eb4c852157b01b94c189/plot_borgonovo_sensitivity.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_borgonovo_sensitivity.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/e830ec759701c1a03cbee2f23827aba1/plot_borgonovo_sensitivity.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_borgonovo_sensitivity.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/91f30700716a0dd0a964e349dc44e117/plot_borgonovo_sensitivity.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_borgonovo_sensitivity.zip</span></code></a></p>
+</div>
+</div>
+</section>
+</section>
+
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="clearer"></div>
+    </div>
+    <div class="related" role="navigation" aria-label="Related">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../genindex.html" title="General Index"
+             >index</a></li>
+        <li class="right" >
+          <a href="plot_ishigami_sensitivity.html" title="Benchmark the Ishigami test function"
+             >next</a> |</li>
+        <li class="right" >
+          <a href="plot_flood_sensitivity.html" title="Benchmark the Flooding test function"
+             >previous</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../index.html">otbenchmark 0.1 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../index.html" >Examples</a> &#187;</li>
+          <li class="nav-item nav-item-2"><a href="index.html" >Sensitivity examples</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">Benchmark the Borgonovo test function</a></li> 
+      </ul>
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+ <script type="text/javascript">
+    $(document).ready(function() {
+        $(".toggle > *").hide();
+        $(".toggle .header").show();
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diff --git a/otbenchmark/master/auto_examples/sensitivity_problems/plot_convergence_ishigami.html b/otbenchmark/master/auto_examples/sensitivity_problems/plot_convergence_ishigami.html
index 562181b28d1..f51793251f0 100644
--- a/otbenchmark/master/auto_examples/sensitivity_problems/plot_convergence_ishigami.html
+++ b/otbenchmark/master/auto_examples/sensitivity_problems/plot_convergence_ishigami.html
@@ -19,8 +19,8 @@
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
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-    <link rel="next" title="Developer guide" href="../../developer_guide/developer_guide.html" />
-    <link rel="prev" title="Print the sensitivity analysis problems" href="plot_print_problems.html" />
+    <link rel="next" title="Benchmark sensitivity analysis methods" href="plot_benchmark_sensitivity_methods.html" />
+    <link rel="prev" title="Benchmark the G-Sobol test function" href="plot_gsobol_sensitivity.html" />
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           <a href="../../genindex.html" title="General Index"
              accesskey="I">index</a></li>
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-          <a href="../../developer_guide/developer_guide.html" title="Developer guide"
+          <a href="plot_benchmark_sensitivity_methods.html" title="Benchmark sensitivity analysis methods"
              accesskey="N">next</a> |</li>
         <li class="right" >
-          <a href="plot_print_problems.html" title="Print the sensitivity analysis problems"
+          <a href="plot_gsobol_sensitivity.html" title="Benchmark the G-Sobol test function"
              accesskey="P">previous</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../index.html">otbenchmark 0.1 documentation</a> &#187;</li>
           <li class="nav-item nav-item-1"><a href="../index.html" >Examples</a> &#187;</li>
@@ -69,20 +69,17 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
 <li class="toctree-l2 current"><a class="reference internal" href="index.html">Sensitivity examples</a></li>
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-    <p class="topless"><a href="plot_print_problems.html"
-                          title="previous chapter">Print the sensitivity analysis problems</a></p>
+    <p class="topless"><a href="plot_gsobol_sensitivity.html"
+                          title="previous chapter">Benchmark the G-Sobol test function</a></p>
   </div>
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     <h4>Next topic</h4>
-    <p class="topless"><a href="../../developer_guide/developer_guide.html"
-                          title="next chapter">Developer guide</a></p>
+    <p class="topless"><a href="plot_benchmark_sensitivity_methods.html"
+                          title="next chapter">Benchmark sensitivity analysis methods</a></p>
   </div>
   <div role="note" aria-label="source link">
     <h3>This Page</h3>
@@ -315,13 +312,13 @@ <h2>Perform sensitivity analysis<a class="headerlink" href="#perform-sensitivity
 Elapsed = 0.1 (s), Sample size = 160
 Elapsed = 0.6 (s), Sample size = 320
 Elapsed = 3.1 (s), Sample size = 640
-Elapsed = 18.60 (s)
+Elapsed = 18.70 (s)
 </pre></div>
 </div>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 44.837 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 45.054 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-convergence-ishigami-py">
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              >index</a></li>
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-          <a href="../../developer_guide/developer_guide.html" title="Developer guide"
+          <a href="plot_benchmark_sensitivity_methods.html" title="Benchmark sensitivity analysis methods"
              >next</a> |</li>
         <li class="right" >
-          <a href="plot_print_problems.html" title="Print the sensitivity analysis problems"
+          <a href="plot_gsobol_sensitivity.html" title="Benchmark the G-Sobol test function"
              >previous</a> |</li>
         <li class="nav-item nav-item-0"><a href="../../index.html">otbenchmark 0.1 documentation</a> &#187;</li>
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diff --git a/otbenchmark/master/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.html b/otbenchmark/master/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.html
new file mode 100644
index 00000000000..e254e50dc24
--- /dev/null
+++ b/otbenchmark/master/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.html
@@ -0,0 +1,278 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../../">
+  <head>
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+    <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
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+    <link rel="next" title="Benchmark the Borehole test function" href="plot_borehole_sensitivity.html" />
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+    <li><a href="../../index.html">Home</a></li>
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+    <h1>
+      <img src="../../_static/logo-openturns-wo-bg.png" alt="" width=100px height=100px />
+      otbenchmark
+    </h1>
+    <h2> Module otbenchmark </h2>
+  </a>
+</div>
+
+    <div class="related" role="navigation" aria-label="Related">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../genindex.html" title="General Index"
+             accesskey="I">index</a></li>
+        <li class="right" >
+          <a href="plot_borehole_sensitivity.html" title="Benchmark the Borehole test function"
+             accesskey="N">next</a> |</li>
+        <li class="right" >
+          <a href="plot_ishigami_sensitivity.html" title="Benchmark the Ishigami test function"
+             accesskey="P">previous</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../index.html">otbenchmark 0.1 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../index.html" >Examples</a> &#187;</li>
+          <li class="nav-item nav-item-2"><a href="index.html" accesskey="U">Sensitivity examples</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">Benchmark the Dirichlet test function</a></li> 
+      </ul>
+    </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="Main">
+        <div class="sphinxsidebarwrapper">
+<h3><a href="../../index.html">Table of Contents</a></h3>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../../user_manual/user_manual.html">API Reference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html#sensitivity-examples">Sensitivity examples</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="index.html">Sensitivity examples</a></li>
+</ul>
+</li>
+</ul>
+
+  <div>
+    <h4>Previous topic</h4>
+    <p class="topless"><a href="plot_ishigami_sensitivity.html"
+                          title="previous chapter">Benchmark the Ishigami test function</a></p>
+  </div>
+  <div>
+    <h4>Next topic</h4>
+    <p class="topless"><a href="plot_borehole_sensitivity.html"
+                          title="next chapter">Benchmark the Borehole test function</a></p>
+  </div>
+  <div role="note" aria-label="source link">
+    <h3>This Page</h3>
+    <ul class="this-page-menu">
+      <li><a href="../../_sources/auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.rst"
+            rel="nofollow">Show Source</a></li>
+    </ul>
+   </div>
+<search id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../search.html" method="get">
+      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
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+    </form>
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+<script>document.getElementById('searchbox').style.display = "block"</script>
+        </div>
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+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-sensitivity-problems-plot-dirichlet-sensitivity-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-dirichlet-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-dirichlet-sensitivity-py"></span><h1>Benchmark the Dirichlet test function<a class="headerlink" href="#benchmark-the-dirichlet-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">DirichletSensitivity</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = Dirichlet
+distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3))
+function = class=PythonEvaluation name=DirichletFunction
+firstOrderIndices = [0.525547,0.233577,0.131387]
+totalOrderIndices = [0.620438,0.310219,0.182482]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">exact_first_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=3 values=[0.525547,0.233577,0.131387]
+</div>
+<br />
+<br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="n">exact_total_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=3 values=[0.620438,0.310219,0.182482]
+</div>
+<br />
+<br /><section id="plot-the-function">
+<h2>Plot the function<a class="headerlink" href="#plot-the-function" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">full_sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimension</span><span class="p">)]</span> <span class="o">=</span> <span class="n">inputDesign</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="n">dimension</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span>
+<span class="n">full_description</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">())</span>
+<span class="n">full_description</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">outputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">full_sample</span><span class="o">.</span><span class="n">setDescription</span><span class="p">(</span><span class="n">full_description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">marginal_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">ComposedDistribution</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">full_sample</span><span class="o">.</span><span class="n">getMarginal</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+<span class="n">clouds</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">VisualTest</span><span class="o">.</span><span class="n">DrawPairsMarginals</span><span class="p">(</span><span class="n">full_sample</span><span class="p">,</span> <span class="n">marginal_distribution</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">clouds</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">)})</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_dirichlet_sensitivity_001.png" srcset="../../_images/sphx_glr_plot_dirichlet_sensitivity_001.png" alt="plot dirichlet sensitivity" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_dirichlet_sensitivity_002.png" srcset="../../_images/sphx_glr_plot_dirichlet_sensitivity_002.png" alt="plot dirichlet sensitivity" class = "sphx-glr-single-img"/></section>
+<section id="perform-sensitivity-analysis">
+<h2>Perform sensitivity analysis<a class="headerlink" href="#perform-sensitivity-analysis" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="c1"># Compute absolute error (the LRE cannot be computed,</span>
+<span class="c1"># because S can be zero)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  10000
+Computed first order =  [0.5332,0.240662,0.130402]
+Exact first order =  [0.525547,0.233577,0.131387]
+Computed total order =  [0.616112,0.301247,0.180961]
+Exact total order =  [0.620438,0.310219,0.182482]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_dirichlet_sensitivity_003.png" srcset="../../_images/sphx_glr_plot_dirichlet_sensitivity_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.273 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-dirichlet-sensitivity-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/82f4b8a8c9c6a4e5332608271aad9cee/plot_dirichlet_sensitivity.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_dirichlet_sensitivity.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/c1babdce08a3967dd83b589429d3cfb4/plot_dirichlet_sensitivity.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_dirichlet_sensitivity.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/09a8c2ecba196a7145cf5887e3bfbf39/plot_dirichlet_sensitivity.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_dirichlet_sensitivity.zip</span></code></a></p>
+</div>
+</div>
+</section>
+</section>
+
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
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+             >index</a></li>
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+             >previous</a> |</li>
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+<!DOCTYPE html>
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+          <li class="nav-item nav-item-2"><a href="index.html" accesskey="U">Sensitivity examples</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">Benchmark the Flooding test function</a></li> 
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+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html#sensitivity-examples">Sensitivity examples</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="index.html">Sensitivity examples</a></li>
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+  <div>
+    <h4>Previous topic</h4>
+    <p class="topless"><a href="plot_print_problems.html"
+                          title="previous chapter">Print the sensitivity analysis problems</a></p>
+  </div>
+  <div>
+    <h4>Next topic</h4>
+    <p class="topless"><a href="plot_borgonovo_sensitivity.html"
+                          title="next chapter">Benchmark the Borgonovo test function</a></p>
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+    <ul class="this-page-menu">
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-sensitivity-problems-plot-flood-sensitivity-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-flooding-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-flood-sensitivity-py"></span><h1>Benchmark the Flooding test function<a class="headerlink" href="#benchmark-the-flooding-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">FloodingSensitivity</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = Flooding
+distribution = ComposedDistribution(TruncatedDistribution(Gumbel(beta = 558, gamma = 1013), bounds = [0, (19000.8) +inf[), TruncatedDistribution(Normal(mu = 30, sigma = 7.5), bounds = [0, (87.3797) +inf[), Uniform(a = 49, b = 51), Uniform(a = 54, b = 56), Uniform(a = 7, b = 9), Triangular(a = 55, m = 55.5, b = 56), Triangular(a = 4990, m = 5000, b = 5010), Triangular(a = 295, m = 300, b = 305), IndependentCopula(dimension = 8))
+function = [Q,Ks,Zv,Zm,Hd,Zb,L,B]-&gt;[(Q / (Ks * B * sqrt((Zm - Zv) / L)))^(3.0 / 5.0) + Zv - Zb - Hd]
+firstOrderIndices = [0.38,0.13,0.25,0,0.19,0.02,0,0]
+totalOrderIndices = [0.4,0.15,0.25,0.01,0.19,0.02,0,0]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">exact_first_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=8 values=[0.38,0.13,0.25,0,0.19,0.02,0,0]
+</div>
+<br />
+<br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="n">exact_total_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=8 values=[0.4,0.15,0.25,0.01,0.19,0.02,0,0]
+</div>
+<br />
+<br /><section id="plot-the-function">
+<h2>Plot the function<a class="headerlink" href="#plot-the-function" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">full_sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimension</span><span class="p">)]</span> <span class="o">=</span> <span class="n">inputDesign</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="n">dimension</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span>
+<span class="n">full_description</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">())</span>
+<span class="n">full_description</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">outputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">full_sample</span><span class="o">.</span><span class="n">setDescription</span><span class="p">(</span><span class="n">full_description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">marginal_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">ComposedDistribution</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">full_sample</span><span class="o">.</span><span class="n">getMarginal</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+<span class="n">clouds</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">VisualTest</span><span class="o">.</span><span class="n">DrawPairsMarginals</span><span class="p">(</span><span class="n">full_sample</span><span class="p">,</span> <span class="n">marginal_distribution</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">clouds</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">10.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)})</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_flood_sensitivity_001.png" srcset="../../_images/sphx_glr_plot_flood_sensitivity_001.png" alt="plot flood sensitivity" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_flood_sensitivity_002.png" srcset="../../_images/sphx_glr_plot_flood_sensitivity_002.png" alt="plot flood sensitivity" class = "sphx-glr-single-img"/></section>
+<section id="perform-sensitivity-analysis">
+<h2>Perform sensitivity analysis<a class="headerlink" href="#perform-sensitivity-analysis" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  10000
+Computed first order =  [0.371175,0.132331,0.26067,0.0122818,0.191486,0.0325675,0.00737938,0.00742294]
+Exact first order =  [0.38,0.13,0.25,0,0.19,0.02,0,0]
+Computed total order =  [0.413371,0.145399,0.24342,0.00473238,0.181701,0.0226346,5.5996e-06,0.000250305]
+Exact total order =  [0.4,0.15,0.25,0.01,0.19,0.02,0,0]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_flood_sensitivity_003.png" srcset="../../_images/sphx_glr_plot_flood_sensitivity_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.451 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-flood-sensitivity-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/39c987f12c082b6f6c2b1c199aed0518/plot_flood_sensitivity.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_flood_sensitivity.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/15180aff63b94e352470b9af8a67b807/plot_flood_sensitivity.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_flood_sensitivity.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/0b4d0577810c77038fc3a798767a76ea/plot_flood_sensitivity.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_flood_sensitivity.zip</span></code></a></p>
+</div>
+</div>
+</section>
+</section>
+
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="clearer"></div>
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+    <div class="related" role="navigation" aria-label="Related">
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+        <li class="right" >
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+             >next</a> |</li>
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+<!DOCTYPE html>
+
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+          <li class="nav-item nav-item-2"><a href="index.html" accesskey="U">Sensitivity examples</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">Benchmark the gaussian product test function</a></li> 
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html#sensitivity-examples">Sensitivity examples</a><ul class="current">
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+    <p class="topless"><a href="plot_gaussian_sum.html"
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+    <p class="topless"><a href="plot_morris_sensitivity.html"
+                          title="next chapter">Benchmark the Morris test function</a></p>
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-sensitivity-problems-plot-gaussian-product-sensitivity-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-gaussian-product-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-gaussian-product-sensitivity-py"></span><h1>Benchmark the gaussian product test function<a class="headerlink" href="#benchmark-the-gaussian-product-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">GaussianProductSensitivity</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = GaussianProduct
+distribution = ComposedDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 2))
+function = class=PythonEvaluation name=OpenTURNSPythonFunction
+firstOrderIndices = [0,0]
+totalOrderIndices = [1,1]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exact_first_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[0,0]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[1,1]
+</pre></div>
+</div>
+<section id="plot-the-function">
+<h2>Plot the function<a class="headerlink" href="#plot-the-function" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">full_sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimension</span><span class="p">)]</span> <span class="o">=</span> <span class="n">inputDesign</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="n">dimension</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span>
+<span class="n">full_description</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">())</span>
+<span class="n">full_description</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">outputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">full_sample</span><span class="o">.</span><span class="n">setDescription</span><span class="p">(</span><span class="n">full_description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">marginal_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">ComposedDistribution</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">full_sample</span><span class="o">.</span><span class="n">getMarginal</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+<span class="n">clouds</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">VisualTest</span><span class="o">.</span><span class="n">DrawPairsMarginals</span><span class="p">(</span><span class="n">full_sample</span><span class="p">,</span> <span class="n">marginal_distribution</span><span class="p">)</span>
+<span class="n">view</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">clouds</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">)})</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_gaussian_product_sensitivity_001.png" srcset="../../_images/sphx_glr_plot_gaussian_product_sensitivity_001.png" alt="plot gaussian product sensitivity" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_gaussian_product_sensitivity_002.png" srcset="../../_images/sphx_glr_plot_gaussian_product_sensitivity_002.png" alt="plot gaussian product sensitivity" class = "sphx-glr-single-img"/></section>
+<section id="perform-sensitivity-analysis">
+<h2>Perform sensitivity analysis<a class="headerlink" href="#perform-sensitivity-analysis" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="c1"># Compute absolute error (the LRE cannot be computed,</span>
+<span class="c1"># because S can be zero)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  10000
+Computed first order =  [0.00444004,-0.030873]
+Exact first order =  [0,0]
+Computed total order =  [1.02181,0.996161]
+Exact total order =  [1,1]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_gaussian_product_sensitivity_003.png" srcset="../../_images/sphx_glr_plot_gaussian_product_sensitivity_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.658 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-gaussian-product-sensitivity-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/3bd92b053e8108a3b84f939606e1f584/plot_gaussian_product_sensitivity.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_gaussian_product_sensitivity.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/5d0b8a3ea31fa859435ac0c7fb178822/plot_gaussian_product_sensitivity.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_gaussian_product_sensitivity.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/9aafdbfee23b48eed34e7b7fb324d32a/plot_gaussian_product_sensitivity.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_gaussian_product_sensitivity.zip</span></code></a></p>
+</div>
+</div>
+</section>
+</section>
+
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+             >index</a></li>
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+    <p class="topless"><a href="plot_nloscillator_sensitivity.html"
+                          title="previous chapter">Benchmark the NLOscillator test function</a></p>
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+    <p class="topless"><a href="plot_gaussian_product_sensitivity.html"
+                          title="next chapter">Benchmark the gaussian product test function</a></p>
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-sensitivity-problems-plot-gaussian-sum-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-gaussian-sum-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-gaussian-sum-py"></span><h1>Benchmark the gaussian sum test function<a class="headerlink" href="#benchmark-the-gaussian-sum-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">GaussianSumSensitivity</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = GaussianSum
+distribution = ComposedDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 2))
+function = class=PythonEvaluation name=OpenTURNSPythonFunction
+firstOrderIndices = [0.5,0.5]
+totalOrderIndices = [0.5,0.5]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exact_first_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[0.5,0.5]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[0.5,0.5]
+</pre></div>
+</div>
+<section id="plot-the-function">
+<h2>Plot the function<a class="headerlink" href="#plot-the-function" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">full_sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimension</span><span class="p">)]</span> <span class="o">=</span> <span class="n">inputDesign</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="n">dimension</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span>
+<span class="n">full_description</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">())</span>
+<span class="n">full_description</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">outputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">full_sample</span><span class="o">.</span><span class="n">setDescription</span><span class="p">(</span><span class="n">full_description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">marginal_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">ComposedDistribution</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">full_sample</span><span class="o">.</span><span class="n">getMarginal</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+<span class="n">clouds</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">VisualTest</span><span class="o">.</span><span class="n">DrawPairsMarginals</span><span class="p">(</span><span class="n">full_sample</span><span class="p">,</span> <span class="n">marginal_distribution</span><span class="p">)</span>
+<span class="n">view</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">clouds</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">)})</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_gaussian_sum_001.png" srcset="../../_images/sphx_glr_plot_gaussian_sum_001.png" alt="plot gaussian sum" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_gaussian_sum_002.png" srcset="../../_images/sphx_glr_plot_gaussian_sum_002.png" alt="plot gaussian sum" class = "sphx-glr-single-img"/></section>
+<section id="perform-sensitivity-analysis">
+<h2>Perform sensitivity analysis<a class="headerlink" href="#perform-sensitivity-analysis" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="c1"># Compute absolute error (the LRE cannot be computed,</span>
+<span class="c1"># because S can be zero)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  10000
+Computed first order =  [0.513373,0.504889]
+Exact first order =  [0.5,0.5]
+Computed total order =  [0.50317,0.494875]
+Exact total order =  [0.5,0.5]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_gaussian_sum_003.png" srcset="../../_images/sphx_glr_plot_gaussian_sum_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.770 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-gaussian-sum-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/aa932f5940d0a0978383c5019f1a7018/plot_gaussian_sum.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_gaussian_sum.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/9ace8485dedabebe7a1c9e1e1009c0ea/plot_gaussian_sum.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_gaussian_sum.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/629687dd8581020f5bdda375e12cf702/plot_gaussian_sum.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_gaussian_sum.zip</span></code></a></p>
+</div>
+</div>
+</section>
+</section>
+
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+             >next</a> |</li>
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diff --git a/otbenchmark/master/auto_examples/sensitivity_problems/plot_gsobol_sensitivity.html b/otbenchmark/master/auto_examples/sensitivity_problems/plot_gsobol_sensitivity.html
new file mode 100644
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+++ b/otbenchmark/master/auto_examples/sensitivity_problems/plot_gsobol_sensitivity.html
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+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../../">
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+    <title>Benchmark the G-Sobol test function &#8212; otbenchmark 0.1 documentation</title>
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+        <li class="nav-item nav-item-0"><a href="../../index.html">otbenchmark 0.1 documentation</a> &#187;</li>
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+          <li class="nav-item nav-item-2"><a href="index.html" accesskey="U">Sensitivity examples</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">Benchmark the G-Sobol test function</a></li> 
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+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html#sensitivity-examples">Sensitivity examples</a><ul class="current">
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+    <p class="topless"><a href="plot_oakleyohagan_sensitivity.html"
+                          title="previous chapter">Benchmark the Oakley-O’Hagan test function</a></p>
+  </div>
+  <div>
+    <h4>Next topic</h4>
+    <p class="topless"><a href="plot_convergence_ishigami.html"
+                          title="next chapter">Convergence of estimators on Ishigami</a></p>
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+    <h3>This Page</h3>
+    <ul class="this-page-menu">
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+            rel="nofollow">Show Source</a></li>
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-sensitivity-problems-plot-gsobol-sensitivity-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-g-sobol-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-gsobol-sensitivity-py"></span><h1>Benchmark the G-Sobol test function<a class="headerlink" href="#benchmark-the-g-sobol-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">GSobolSensitivity</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = GSobol
+distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3))
+function = class=PythonEvaluation name=OpenTURNSPythonFunction
+firstOrderIndices = [0.986712,0.00986712,9.86712e-05]
+totalOrderIndices = [0.990034,0.0131566,0.000132]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">exact_first_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=3 values=[0.986712,0.00986712,9.86712e-05]
+</div>
+<br />
+<br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[0.990034,0.0131566,0.000132]
+</pre></div>
+</div>
+<section id="plot-function">
+<h2>Plot function<a class="headerlink" href="#plot-function" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">full_sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimension</span><span class="p">)]</span> <span class="o">=</span> <span class="n">inputDesign</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="n">dimension</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span>
+<span class="n">full_description</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">())</span>
+<span class="n">full_description</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">outputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">full_sample</span><span class="o">.</span><span class="n">setDescription</span><span class="p">(</span><span class="n">full_description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">marginal_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">ComposedDistribution</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">full_sample</span><span class="o">.</span><span class="n">getMarginal</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+<span class="n">clouds</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">VisualTest</span><span class="o">.</span><span class="n">DrawPairsMarginals</span><span class="p">(</span><span class="n">full_sample</span><span class="p">,</span> <span class="n">marginal_distribution</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">clouds</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">)})</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_gsobol_sensitivity_001.png" srcset="../../_images/sphx_glr_plot_gsobol_sensitivity_001.png" alt="plot gsobol sensitivity" class = "sphx-glr-single-img"/><p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">1000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_gsobol_sensitivity_002.png" srcset="../../_images/sphx_glr_plot_gsobol_sensitivity_002.png" alt="plot gsobol sensitivity" class = "sphx-glr-single-img"/></section>
+<section id="perform-sa">
+<h2>Perform SA<a class="headerlink" href="#perform-sa" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="c1"># Compute absolute error (the LRE cannot be computed,</span>
+<span class="c1"># because S can be zero)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  10000
+Computed first order =  [0.978665,0.021486,0.0124414]
+Exact first order =  [0.986712,0.00986712,9.86712e-05]
+Computed total order =  [0.979669,0.0104535,-4.12658e-05]
+Exact total order =  [0.990034,0.0131566,0.000132]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_gsobol_sensitivity_003.png" srcset="../../_images/sphx_glr_plot_gsobol_sensitivity_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.334 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-gsobol-sensitivity-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/643785dea4d339bb780f3446d3c50609/plot_gsobol_sensitivity.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_gsobol_sensitivity.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/be33b4907cde40d05f3bc08b57b462fc/plot_gsobol_sensitivity.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_gsobol_sensitivity.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/3f2f5008640b2a9bf5fb56d17e3345fe/plot_gsobol_sensitivity.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_gsobol_sensitivity.zip</span></code></a></p>
+</div>
+</div>
+</section>
+</section>
+
+
+            <div class="clearer"></div>
+          </div>
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+<!DOCTYPE html>
+
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+    <p class="topless"><a href="plot_borgonovo_sensitivity.html"
+                          title="previous chapter">Benchmark the Borgonovo test function</a></p>
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+    <p class="topless"><a href="plot_dirichlet_sensitivity.html"
+                          title="next chapter">Benchmark the Dirichlet test function</a></p>
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-sensitivity-problems-plot-ishigami-sensitivity-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-ishigami-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-ishigami-sensitivity-py"></span><h1>Benchmark the Ishigami test function<a class="headerlink" href="#benchmark-the-ishigami-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">IshigamiSensitivity</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = Ishigami
+distribution = ComposedDistribution(Uniform(a = -3.14159, b = 3.14159), Uniform(a = -3.14159, b = 3.14159), Uniform(a = -3.14159, b = 3.14159), IndependentCopula(dimension = 3))
+function = ParametricEvaluation([X1,X2,X3,a,b]-&gt;[sin(X1) + a * sin(X2)^2 + b * X3^4 * sin(X1)], parameters positions=[3,4], parameters=[a : 7, b : 0.1], input positions=[0,1,2])
+firstOrderIndices = [0.313905,0.442411,0]
+totalOrderIndices = [0.557589,0.442411,0.243684]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">exact_first_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=3 values=[0.313905,0.442411,0]
+</div>
+<br />
+<br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="n">exact_total_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=3 values=[0.557589,0.442411,0.243684]
+</div>
+<br />
+<br /><section id="plot-the-function">
+<h2>Plot the function<a class="headerlink" href="#plot-the-function" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">full_sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimension</span><span class="p">)]</span> <span class="o">=</span> <span class="n">inputDesign</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="n">dimension</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span>
+<span class="n">full_description</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">())</span>
+<span class="n">full_description</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">outputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">full_sample</span><span class="o">.</span><span class="n">setDescription</span><span class="p">(</span><span class="n">full_description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">marginal_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">ComposedDistribution</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">full_sample</span><span class="o">.</span><span class="n">getMarginal</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+<span class="n">clouds</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">VisualTest</span><span class="o">.</span><span class="n">DrawPairsMarginals</span><span class="p">(</span><span class="n">full_sample</span><span class="p">,</span> <span class="n">marginal_distribution</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">clouds</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">)})</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_ishigami_sensitivity_001.png" srcset="../../_images/sphx_glr_plot_ishigami_sensitivity_001.png" alt="plot ishigami sensitivity" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_ishigami_sensitivity_002.png" srcset="../../_images/sphx_glr_plot_ishigami_sensitivity_002.png" alt="plot ishigami sensitivity" class = "sphx-glr-single-img"/></section>
+<section id="perform-sensitivity-analysis">
+<h2>Perform sensitivity analysis<a class="headerlink" href="#perform-sensitivity-analysis" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="c1"># Compute absolute error (the LRE cannot be computed,</span>
+<span class="c1"># because S can be zero)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  10000
+Computed first order =  [0.302745,0.460846,0.0066916]
+Exact first order =  [0.313905,0.442411,0]
+Computed total order =  [0.574996,0.427126,0.256689]
+Exact total order =  [0.557589,0.442411,0.243684]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_ishigami_sensitivity_003.png" srcset="../../_images/sphx_glr_plot_ishigami_sensitivity_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.187 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-ishigami-sensitivity-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/af08bf3d5aa390154f02f9673b9ee5cb/plot_ishigami_sensitivity.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_ishigami_sensitivity.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/3b23190f79b9a4829b68307be5e836d7/plot_ishigami_sensitivity.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_ishigami_sensitivity.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/5fab7882172318ff3e3ffcf48ef9eddf/plot_ishigami_sensitivity.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_ishigami_sensitivity.zip</span></code></a></p>
+</div>
+</div>
+</section>
+</section>
+
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
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+             >next</a> |</li>
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+             >previous</a> |</li>
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diff --git a/otbenchmark/master/auto_examples/sensitivity_problems/plot_morris_sensitivity.html b/otbenchmark/master/auto_examples/sensitivity_problems/plot_morris_sensitivity.html
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+<!DOCTYPE html>
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+    <p class="topless"><a href="plot_oakleyohagan_sensitivity.html"
+                          title="next chapter">Benchmark the Oakley-O’Hagan test function</a></p>
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+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-sensitivity-problems-plot-morris-sensitivity-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-morris-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-morris-sensitivity-py"></span><h1>Benchmark the Morris test function<a class="headerlink" href="#benchmark-the-morris-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">MorrisSensitivity</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = Morris
+distribution = ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 20))
+function = class=PythonEvaluation name=MorrisFunction
+firstOrderIndices = [0.08,0.08,0.06,0.08,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20
+totalOrderIndices = [0.11,0.11,0.06,0.11,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">exact_first_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=20 values=[0.08,0.08,0.06,0.08,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]
+</div>
+<br />
+<br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="n">exact_total_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=20 values=[0.11,0.11,0.06,0.11,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]
+</div>
+<br />
+<br /><section id="plot-the-function">
+<h2>Plot the function<a class="headerlink" href="#plot-the-function" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">nbcolumns</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">nbrows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">dimension</span> <span class="o">/</span> <span class="n">nbcolumns</span><span class="p">)</span>
+<span class="n">grid</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">GridLayout</span><span class="p">(</span><span class="n">nbrows</span><span class="p">,</span> <span class="n">nbcolumns</span><span class="p">)</span>
+<span class="n">inputDescription</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()</span>
+<span class="n">outputDescription</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">getOutputDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nbrows</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nbcolumns</span><span class="p">):</span>
+        <span class="n">graph</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span>
+            <span class="s2">&quot;n=</span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">size</span><span class="p">),</span> <span class="n">inputDescription</span><span class="p">[</span><span class="n">index</span><span class="p">],</span> <span class="n">outputDescription</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">&quot;&quot;</span>
+        <span class="p">)</span>
+        <span class="n">sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">sample</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">inputDesign</span><span class="p">[:,</span> <span class="n">index</span><span class="p">]</span>
+        <span class="n">sample</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="n">cloud</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Cloud</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+        <span class="n">graph</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">cloud</span><span class="p">)</span>
+        <span class="n">grid</span><span class="o">.</span><span class="n">setGraph</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">graph</span><span class="p">)</span>
+        <span class="n">index</span> <span class="o">+=</span> <span class="mi">1</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">grid</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">10.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)})</span>
+
+<span class="c1"># %</span>
+<span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_morris_sensitivity_001.png" srcset="../../_images/sphx_glr_plot_morris_sensitivity_001.png" alt=", n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_morris_sensitivity_002.png" srcset="../../_images/sphx_glr_plot_morris_sensitivity_002.png" alt="plot morris sensitivity" class = "sphx-glr-multi-img"/></li>
+</ul>
+</section>
+<section id="perform-sensitivity-analysis">
+<h2>Perform sensitivity analysis<a class="headerlink" href="#perform-sensitivity-analysis" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">30</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="c1"># Compute absolute error (the LRE cannot be computed,</span>
+<span class="c1"># because S can be zero)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  30
+Computed first order =  [0.0607395,0.20637,0.267428,0.262258,0.314109,0.61827,0.294943,0.439378,0.246433,0.274413,0.221509,0.18807,0.218819,0.228214,0.208058,0.215507,0.23585,0.21719,0.237087,0.214426]#20
+Exact first order =  [0.08,0.08,0.06,0.08,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20
+Computed total order =  [0.0311589,0.22498,0.219259,0.078154,-0.0539979,-0.0392477,-0.0216198,0.20086,0.311105,0.0877451,-0.0160964,-0.000247103,-0.0276497,0.000406818,-0.0165498,-0.0183569,-0.0308313,-0.0264689,-0.00283312,-0.00660521]#20
+Exact total order =  [0.11,0.11,0.06,0.11,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_morris_sensitivity_003.png" srcset="../../_images/sphx_glr_plot_morris_sensitivity_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 16.944 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-morris-sensitivity-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/d005867b51360ee9c2c4914f27adb11b/plot_morris_sensitivity.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_morris_sensitivity.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/a707b852149a93fcecac6a74f1fed670/plot_morris_sensitivity.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_morris_sensitivity.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/66b4d9d63c28c108996cfe858ca9289e/plot_morris_sensitivity.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_morris_sensitivity.zip</span></code></a></p>
+</div>
+</div>
+</section>
+</section>
+
+
+            <div class="clearer"></div>
+          </div>
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+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html#sensitivity-examples">Sensitivity examples</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="index.html">Sensitivity examples</a></li>
+</ul>
+</li>
+</ul>
+
+  <div>
+    <h4>Previous topic</h4>
+    <p class="topless"><a href="plot_borehole_sensitivity.html"
+                          title="previous chapter">Benchmark the Borehole test function</a></p>
+  </div>
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+    <h4>Next topic</h4>
+    <p class="topless"><a href="plot_gaussian_sum.html"
+                          title="next chapter">Benchmark the gaussian sum test function</a></p>
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-sensitivity-problems-plot-nloscillator-sensitivity-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-nloscillator-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-nloscillator-sensitivity-py"></span><h1>Benchmark the NLOscillator test function<a class="headerlink" href="#benchmark-the-nloscillator-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">NLOscillatorSensitivity</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = N.L. Oscillator
+distribution = ComposedDistribution(class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=21.5 sigma=2.15 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=3.06308 sigmaLog=0.0997513 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=1.5 sigma=0.15 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=0.40049 sigmaLog=0.0997513 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.01 sigma=0.001 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-4.61015 sigmaLog=0.0997513 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=1 sigma=0.2 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-0.0196104 sigmaLog=0.198042 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.01 sigma=0.002 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-4.62478 sigmaLog=0.198042 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.05 sigma=0.02 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-3.06994 sigmaLog=0.385253 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=0.02 sigma=0.01 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=-4.02359 sigmaLog=0.472381 gamma=0, class=ParametrizedDistribution parameters=class=LogNormalMuSigma name=Unnamed mu=100 sigma=10 gamma=0 distribution=class=LogNormal name=LogNormal dimension=1 muLog=4.6002 sigmaLog=0.0997513 gamma=0, IndependentCopula(dimension = 8))
+function = class=PythonEvaluation name=OpenTURNSPythonFunction
+firstOrderIndices = [0.4,0.03,0.09,0.18,0.12,0.05,0.05,0]
+totalOrderIndices = [0.4,0.04,0.1,0.23,0.16,0.07,0.06,0.01]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">exact_first_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=8 values=[0.4,0.03,0.09,0.18,0.12,0.05,0.05,0]
+</div>
+<br />
+<br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="n">exact_total_order</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+class=Point name=Unnamed dimension=8 values=[0.4,0.04,0.1,0.23,0.16,0.07,0.06,0.01]
+</div>
+<br />
+<br /><section id="plot-the-function">
+<h2>Plot the function<a class="headerlink" href="#plot-the-function" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">full_sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="nb">range</span><span class="p">(</span><span class="n">dimension</span><span class="p">)]</span> <span class="o">=</span> <span class="n">inputDesign</span>
+<span class="n">full_sample</span><span class="p">[:,</span> <span class="n">dimension</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span>
+<span class="n">full_description</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">())</span>
+<span class="n">full_description</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">outputDesign</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">full_sample</span><span class="o">.</span><span class="n">setDescription</span><span class="p">(</span><span class="n">full_description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">marginal_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">ComposedDistribution</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">full_sample</span><span class="o">.</span><span class="n">getMarginal</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dimension</span><span class="p">)</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+<span class="n">clouds</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">VisualTest</span><span class="o">.</span><span class="n">DrawPairsMarginals</span><span class="p">(</span><span class="n">full_sample</span><span class="p">,</span> <span class="n">marginal_distribution</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">clouds</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">10.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)})</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_nloscillator_sensitivity_001.png" srcset="../../_images/sphx_glr_plot_nloscillator_sensitivity_001.png" alt="plot nloscillator sensitivity" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_nloscillator_sensitivity_002.png" srcset="../../_images/sphx_glr_plot_nloscillator_sensitivity_002.png" alt="plot nloscillator sensitivity" class = "sphx-glr-single-img"/></section>
+<section id="perform-sensitivity-analysis">
+<h2>Perform sensitivity analysis<a class="headerlink" href="#perform-sensitivity-analysis" title="Link to this heading">¶</a></h2>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="c1"># Compute absolute error (the LRE cannot be computed,</span>
+<span class="c1"># because S can be zero)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  10000
+Computed first order =  [0.374552,0.0347919,0.0853174,0.186246,0.120437,0.04356,0.0358369,0.00467826]
+Exact first order =  [0.4,0.03,0.09,0.18,0.12,0.05,0.05,0]
+Computed total order =  [0.375092,0.0778412,0.127126,0.279952,0.201389,0.0667096,0.0458004,0.00812321]
+Exact total order =  [0.4,0.04,0.1,0.23,0.16,0.07,0.06,0.01]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_nloscillator_sensitivity_003.png" srcset="../../_images/sphx_glr_plot_nloscillator_sensitivity_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 3.807 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-nloscillator-sensitivity-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/86db06e71453f8056914ef0dcd6b0132/plot_nloscillator_sensitivity.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_nloscillator_sensitivity.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/ac791b18603462477ad30d69d47bc91f/plot_nloscillator_sensitivity.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_nloscillator_sensitivity.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/02ebaaff0fbfc2d6c398818b1ca6b2f8/plot_nloscillator_sensitivity.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_nloscillator_sensitivity.zip</span></code></a></p>
+</div>
+</div>
+</section>
+</section>
+
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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+             >index</a></li>
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+          <a href="plot_gaussian_sum.html" title="Benchmark the gaussian sum test function"
+             >next</a> |</li>
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+             >previous</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../index.html">otbenchmark 0.1 documentation</a> &#187;</li>
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+          <li class="nav-item nav-item-2"><a href="index.html" >Sensitivity examples</a> &#187;</li>
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+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmark-the-oakley-o-hagan-test-function">
+<span id="sphx-glr-auto-examples-sensitivity-problems-plot-oakleyohagan-sensitivity-py"></span><h1>Benchmark the Oakley-O’Hagan test function<a class="headerlink" href="#benchmark-the-oakley-o-hagan-test-function" title="Link to this heading">¶</a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openturns</span> <span class="k">as</span> <span class="nn">ot</span>
+<span class="kn">import</span> <span class="nn">otbenchmark</span> <span class="k">as</span> <span class="nn">otb</span>
+<span class="kn">import</span> <span class="nn">openturns.viewer</span> <span class="k">as</span> <span class="nn">otv</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">problem</span> <span class="o">=</span> <span class="n">otb</span><span class="o">.</span><span class="n">OakleyOHaganSensitivity</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">problem</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>name = Oakley-O&#39;Hagan
+distribution = ComposedDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 15))
+function = class=PythonEvaluation name=OpenTURNSPythonFunction
+firstOrderIndices = [0,0,0,0,0,0.02,0.02,0.03,0.05,0.01,0.1,0.14,0.1,0.11,0.12]#15
+totalOrderIndices = [0.06,0.06,0.04,0.05,0.02,0.04,0.06,0.08,0.1,0.04,0.15,0.15,0.14,0.14,0.16]#15
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">distribution</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getInputDistribution</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFunction</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Exact first and total order</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_first_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exact_first_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[0,0,0,0,0,0.02,0.02,0.03,0.05,0.01,0.1,0.14,0.1,0.11,0.12]#15
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">exact_total_order</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exact_total_order</span><span class="p">)</span>
+
+
+<span class="c1"># ## Plot the function</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[0.06,0.06,0.04,0.05,0.02,0.04,0.06,0.08,0.1,0.04,0.15,0.15,0.14,0.14,0.16]#15
+</pre></div>
+</div>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">MonteCarloExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dimension</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDimension</span><span class="p">()</span>
+<span class="n">nbcolumns</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">nbrows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">dimension</span> <span class="o">/</span> <span class="n">nbcolumns</span><span class="p">)</span>
+<span class="n">grid</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">GridLayout</span><span class="p">(</span><span class="n">nbrows</span><span class="p">,</span> <span class="n">nbcolumns</span><span class="p">)</span>
+<span class="n">inputDescription</span> <span class="o">=</span> <span class="n">distribution</span><span class="o">.</span><span class="n">getDescription</span><span class="p">()</span>
+<span class="n">outputDescription</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">getOutputDescription</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nbrows</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nbcolumns</span><span class="p">):</span>
+        <span class="n">graph</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span>
+            <span class="s2">&quot;n=</span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">size</span><span class="p">),</span> <span class="n">inputDescription</span><span class="p">[</span><span class="n">index</span><span class="p">],</span> <span class="n">outputDescription</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">&quot;&quot;</span>
+        <span class="p">)</span>
+        <span class="n">sample</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Sample</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">sample</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">inputDesign</span><span class="p">[:,</span> <span class="n">index</span><span class="p">]</span>
+        <span class="n">sample</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputDesign</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="n">cloud</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">Cloud</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+        <span class="n">graph</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">cloud</span><span class="p">)</span>
+        <span class="n">grid</span><span class="o">.</span><span class="n">setGraph</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">graph</span><span class="p">)</span>
+        <span class="n">index</span> <span class="o">+=</span> <span class="mi">1</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">grid</span><span class="p">,</span> <span class="n">figure_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;figsize&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">10.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)})</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_oakleyohagan_sensitivity_001.png" srcset="../../_images/sphx_glr_plot_oakleyohagan_sensitivity_001.png" alt=", n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200, n=200" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_distribution</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">KernelSmoothing</span><span class="p">()</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">outputDesign</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">.</span><span class="n">drawPDF</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_oakleyohagan_sensitivity_002.png" srcset="../../_images/sphx_glr_plot_oakleyohagan_sensitivity_002.png" alt="plot oakleyohagan sensitivity" class = "sphx-glr-single-img"/><p>## Perform sensitivity analysis</p>
+<p>Create X/Y data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">ot</span><span class="o">.</span><span class="n">RandomGenerator</span><span class="o">.</span><span class="n">SetSeed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">size</span> <span class="o">=</span> <span class="mi">1000</span>
+<span class="n">inputDesign</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SobolIndicesExperiment</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">()</span>
+<span class="n">outputDesign</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute first order indices using the Saltelli estimator</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">sensitivityAnalysis</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">SaltelliSensitivityAlgorithm</span><span class="p">(</span><span class="n">inputDesign</span><span class="p">,</span> <span class="n">outputDesign</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="n">computed_first_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getFirstOrderIndices</span><span class="p">()</span>
+<span class="n">computed_total_order</span> <span class="o">=</span> <span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">getTotalOrderIndices</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Compare with exact results</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sample size : &quot;</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+<span class="c1"># First order</span>
+<span class="c1"># Compute absolute error (the LRE cannot be computed,</span>
+<span class="c1"># because S can be zero)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed first order = &quot;</span><span class="p">,</span> <span class="n">computed_first_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact first order = &quot;</span><span class="p">,</span> <span class="n">exact_first_order</span><span class="p">)</span>
+<span class="c1"># Total order</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Computed total order = &quot;</span><span class="p">,</span> <span class="n">computed_total_order</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exact total order = &quot;</span><span class="p">,</span> <span class="n">exact_total_order</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sample size :  1000
+Computed first order =  [0.00686154,0.0173649,0.0121877,-0.0153947,-0.0223964,0.0038293,0.0373239,0.014391,0.0568532,0.0057647,0.0867005,0.162502,0.0882588,0.0706715,0.108779]#15
+Exact first order =  [0,0,0,0,0,0.02,0.02,0.03,0.05,0.01,0.1,0.14,0.1,0.11,0.12]#15
+Computed total order =  [0.0565808,0.0625981,0.0461935,0.0758801,0.0356766,0.028928,0.058367,0.101891,0.087846,0.0501691,0.128598,0.146953,0.125396,0.149566,0.16276]#15
+Exact total order =  [0.06,0.06,0.04,0.05,0.02,0.04,0.06,0.08,0.1,0.04,0.15,0.15,0.14,0.14,0.16]#15
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="p">(</span><span class="n">sensitivityAnalysis</span><span class="o">.</span><span class="n">draw</span><span class="p">())</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_oakleyohagan_sensitivity_003.png" srcset="../../_images/sphx_glr_plot_oakleyohagan_sensitivity_003.png" alt="Sobol' indices - SaltelliSensitivityAlgorithm" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">otv</span><span class="o">.</span><span class="n">View</span><span class="o">.</span><span class="n">ShowAll</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.088 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-sensitivity-problems-plot-oakleyohagan-sensitivity-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/e9791c5b042243aaaed5b1d350b1cf7d/plot_oakleyohagan_sensitivity.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_oakleyohagan_sensitivity.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/7cff43685509138d3c4a3ae1a2644d62/plot_oakleyohagan_sensitivity.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_oakleyohagan_sensitivity.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/e9685936f0e9649b1b8ff6dcb9f0fc2b/plot_oakleyohagan_sensitivity.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_oakleyohagan_sensitivity.zip</span></code></a></p>
+</div>
+</div>
+</section>
+
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
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+      <h3>Navigation</h3>
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+             >index</a></li>
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+        <li class="right" >
+          <a href="plot_morris_sensitivity.html" title="Benchmark the Morris test function"
+             >previous</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../index.html">otbenchmark 0.1 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../index.html" >Examples</a> &#187;</li>
+          <li class="nav-item nav-item-2"><a href="index.html" >Sensitivity examples</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">Benchmark the Oakley-O’Hagan test function</a></li> 
+      </ul>
+    </div>
+ <script type="text/javascript">
+    $(document).ready(function() {
+        $(".toggle > *").hide();
+        $(".toggle .header").show();
+        $(".toggle .header").click(function() {
+            $(this).parent().children().not(".header").toggle(400);
+            $(this).parent().children(".header").toggleClass("open");
+        })
+    });
+</script>
+
+  </body>
+</html>
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diff --git a/otbenchmark/master/auto_examples/sensitivity_problems/plot_print_problems.html b/otbenchmark/master/auto_examples/sensitivity_problems/plot_print_problems.html
index a47fc4d4592..46623baac56 100644
--- a/otbenchmark/master/auto_examples/sensitivity_problems/plot_print_problems.html
+++ b/otbenchmark/master/auto_examples/sensitivity_problems/plot_print_problems.html
@@ -19,7 +19,7 @@
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Convergence of estimators on Ishigami" href="plot_convergence_ishigami.html" />
+    <link rel="next" title="Benchmark the Flooding test function" href="plot_flood_sensitivity.html" />
     <link rel="prev" title="Sensitivity examples" href="index.html" />
     <link href='http://fonts.googleapis.com/css?family=Open+Sans:300,400,700'
           rel='stylesheet' type='text/css' />
@@ -48,7 +48,7 @@ <h3>Navigation</h3>
           <a href="../../genindex.html" title="General Index"
              accesskey="I">index</a></li>
         <li class="right" >
-          <a href="plot_convergence_ishigami.html" title="Convergence of estimators on Ishigami"
+          <a href="plot_flood_sensitivity.html" title="Benchmark the Flooding test function"
              accesskey="N">next</a> |</li>
         <li class="right" >
           <a href="index.html" title="Sensitivity examples"
@@ -69,9 +69,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
 <li class="toctree-l2 current"><a class="reference internal" href="index.html">Sensitivity examples</a></li>
 </ul>
 </li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
@@ -81,8 +78,8 @@ <h4>Previous topic</h4>
   </div>
   <div>
     <h4>Next topic</h4>
-    <p class="topless"><a href="plot_convergence_ishigami.html"
-                          title="next chapter">Convergence of estimators on Ishigami</a></p>
+    <p class="topless"><a href="plot_flood_sensitivity.html"
+                          title="next chapter">Benchmark the Flooding test function</a></p>
   </div>
   <div role="note" aria-label="source link">
     <h3>This Page</h3>
@@ -200,7 +197,7 @@ <h3 id="searchlabel">Quick search</h3>
   latex_code = df_problems_list.to_latex()
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.165 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.252 seconds)</p>
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 <p><a class="reference download internal" download="" href="../../_downloads/bb6ea326a24a084d0534b0ee15332f62/plot_print_problems.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_print_problems.ipynb</span></code></a></p>
@@ -228,7 +225,7 @@ <h3>Navigation</h3>
           <a href="../../genindex.html" title="General Index"
              >index</a></li>
         <li class="right" >
-          <a href="plot_convergence_ishigami.html" title="Convergence of estimators on Ishigami"
+          <a href="plot_flood_sensitivity.html" title="Benchmark the Flooding test function"
              >next</a> |</li>
         <li class="right" >
           <a href="index.html" title="Sensitivity examples"
diff --git a/otbenchmark/master/auto_examples/sensitivity_problems/sg_execution_times.html b/otbenchmark/master/auto_examples/sensitivity_problems/sg_execution_times.html
index 589af592033..0e25a0d0e6b 100644
--- a/otbenchmark/master/auto_examples/sensitivity_problems/sg_execution_times.html
+++ b/otbenchmark/master/auto_examples/sensitivity_problems/sg_execution_times.html
@@ -56,9 +56,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
 <li class="toctree-l1"><a class="reference internal" href="../../user_manual/user_manual.html">API Reference</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div role="note" aria-label="source link">
@@ -88,7 +85,7 @@ <h3 id="searchlabel">Quick search</h3>
             
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-sensitivity-problems-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">¶</a></h1>
-<p><strong>00:45.002</strong> total execution time for 2 files <strong>from auto_examples/sensitivity_problems</strong>:</p>
+<p><strong>01:23.694</strong> total execution time for 14 files <strong>from auto_examples/sensitivity_problems</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -110,11 +107,59 @@ <h3 id="searchlabel">Quick search</h3>
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_convergence_ishigami.html#sphx-glr-auto-examples-sensitivity-problems-plot-convergence-ishigami-py"><span class="std std-ref">Convergence of estimators on Ishigami</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_convergence_ishigami.py</span></code>)</p></td>
-<td><p>00:44.837</p></td>
+<td><p>00:45.054</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_morris_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-morris-sensitivity-py"><span class="std std-ref">Benchmark the Morris test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_morris_sensitivity.py</span></code>)</p></td>
+<td><p>00:16.944</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_nloscillator_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-nloscillator-sensitivity-py"><span class="std std-ref">Benchmark the NLOscillator test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_nloscillator_sensitivity.py</span></code>)</p></td>
+<td><p>00:03.807</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_borehole_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-borehole-sensitivity-py"><span class="std std-ref">Benchmark the Borehole test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_borehole_sensitivity.py</span></code>)</p></td>
+<td><p>00:03.696</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_flood_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-flood-sensitivity-py"><span class="std std-ref">Benchmark the Flooding test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_flood_sensitivity.py</span></code>)</p></td>
+<td><p>00:02.451</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_gsobol_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-gsobol-sensitivity-py"><span class="std std-ref">Benchmark the G-Sobol test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_gsobol_sensitivity.py</span></code>)</p></td>
+<td><p>00:02.334</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_dirichlet_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-dirichlet-sensitivity-py"><span class="std std-ref">Benchmark the Dirichlet test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dirichlet_sensitivity.py</span></code>)</p></td>
+<td><p>00:02.273</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_gaussian_sum.html#sphx-glr-auto-examples-sensitivity-problems-plot-gaussian-sum-py"><span class="std std-ref">Benchmark the gaussian sum test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_gaussian_sum.py</span></code>)</p></td>
+<td><p>00:01.770</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_gaussian_product_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-gaussian-product-sensitivity-py"><span class="std std-ref">Benchmark the gaussian product test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_gaussian_product_sensitivity.py</span></code>)</p></td>
+<td><p>00:01.658</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_ishigami_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-ishigami-sensitivity-py"><span class="std std-ref">Benchmark the Ishigami test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_ishigami_sensitivity.py</span></code>)</p></td>
+<td><p>00:01.187</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_borgonovo_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-borgonovo-sensitivity-py"><span class="std std-ref">Benchmark the Borgonovo test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_borgonovo_sensitivity.py</span></code>)</p></td>
+<td><p>00:01.145</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_oakleyohagan_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-oakleyohagan-sensitivity-py"><span class="std std-ref">Benchmark the Oakley-O’Hagan test function</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_oakleyohagan_sensitivity.py</span></code>)</p></td>
+<td><p>00:01.088</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_print_problems.html#sphx-glr-auto-examples-sensitivity-problems-plot-print-problems-py"><span class="std std-ref">Print the sensitivity analysis problems</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_print_problems.py</span></code>)</p></td>
+<td><p>00:00.252</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_print_problems.html#sphx-glr-auto-examples-sensitivity-problems-plot-print-problems-py"><span class="std std-ref">Print the sensitivity analysis problems</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_print_problems.py</span></code>)</p></td>
-<td><p>00:00.165</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_benchmark_sensitivity_methods.html#sphx-glr-auto-examples-sensitivity-problems-plot-benchmark-sensitivity-methods-py"><span class="std std-ref">Benchmark sensitivity analysis methods</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_benchmark_sensitivity_methods.py</span></code>)</p></td>
+<td><p>00:00.034</p></td>
 <td><p>0.0</p></td>
 </tr>
 </tbody>
diff --git a/otbenchmark/master/auto_examples/sg_execution_times.html b/otbenchmark/master/auto_examples/sg_execution_times.html
index 343ba6f381e..fc963a716ef 100644
--- a/otbenchmark/master/auto_examples/sg_execution_times.html
+++ b/otbenchmark/master/auto_examples/sg_execution_times.html
@@ -56,9 +56,6 @@ <h3><a href="../index.html">Table of Contents</a></h3>
 <li class="toctree-l1"><a class="reference internal" href="../user_manual/user_manual.html">API Reference</a></li>
 <li class="toctree-l1"><a class="reference internal" href="index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div role="note" aria-label="source link">
@@ -88,7 +85,7 @@ <h3 id="searchlabel">Quick search</h3>
             
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">¶</a></h1>
-<p><strong>00:24.333</strong> total execution time for 6 files <strong>from auto_examples</strong>:</p>
+<p><strong>00:23.239</strong> total execution time for 6 files <strong>from auto_examples</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -110,27 +107,27 @@ <h3 id="searchlabel">Quick search</h3>
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_crosscut_distribution_3d.html#sphx-glr-auto-examples-plot-crosscut-distribution-3d-py"><span class="std std-ref">Cross-cuts of conditional distributions in 3-d</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_crosscut_distribution_3d.py</span></code>)</p></td>
-<td><p>00:08.366</p></td>
+<td><p>00:07.724</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_ConditionalDistribution_Demo.html#sphx-glr-auto-examples-plot-conditionaldistribution-demo-py"><span class="std std-ref">Conditional distributions</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_ConditionalDistribution_Demo.py</span></code>)</p></td>
-<td><p>00:04.619</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_methodFactory.html#sphx-glr-auto-examples-plot-methodfactory-py"><span class="std std-ref">Demonstration of the Factory classes for reliability problems</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_methodFactory.py</span></code>)</p></td>
+<td><p>00:07.465</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_methodFactory.html#sphx-glr-auto-examples-plot-methodfactory-py"><span class="std std-ref">Demonstration of the Factory classes for reliability problems</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_methodFactory.py</span></code>)</p></td>
-<td><p>00:04.439</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_ConditionalDistribution_Demo.html#sphx-glr-auto-examples-plot-conditionaldistribution-demo-py"><span class="std std-ref">Conditional distributions</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_ConditionalDistribution_Demo.py</span></code>)</p></td>
+<td><p>00:02.577</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_crosscut_distribution_2d.html#sphx-glr-auto-examples-plot-crosscut-distribution-2d-py"><span class="std std-ref">Cross-cuts of conditional distributions in 2-d</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_crosscut_distribution_2d.py</span></code>)</p></td>
-<td><p>00:03.997</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_convergence_reliability_mc.html#sphx-glr-auto-examples-plot-convergence-reliability-mc-py"><span class="std std-ref">Convergence of Monte-Carlo to estimate the probability in a reliability problem</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_convergence_reliability_mc.py</span></code>)</p></td>
+<td><p>00:02.177</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_convergence_reliability_mc.html#sphx-glr-auto-examples-plot-convergence-reliability-mc-py"><span class="std std-ref">Convergence of Monte-Carlo to estimate the probability in a reliability problem</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_convergence_reliability_mc.py</span></code>)</p></td>
-<td><p>00:02.174</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_crosscut_distribution_2d.html#sphx-glr-auto-examples-plot-crosscut-distribution-2d-py"><span class="std std-ref">Cross-cuts of conditional distributions in 2-d</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_crosscut_distribution_2d.py</span></code>)</p></td>
+<td><p>00:02.066</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_CrossCutFunction_Demo.html#sphx-glr-auto-examples-plot-crosscutfunction-demo-py"><span class="std std-ref">Draw cross-cuts of multidimensional functions</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_CrossCutFunction_Demo.py</span></code>)</p></td>
-<td><p>00:00.738</p></td>
+<td><p>00:01.229</p></td>
 <td><p>0.0</p></td>
 </tr>
 </tbody>
diff --git a/otbenchmark/master/genindex.html b/otbenchmark/master/genindex.html
index f6fff37d14a..91f54809c3b 100644
--- a/otbenchmark/master/genindex.html
+++ b/otbenchmark/master/genindex.html
@@ -55,9 +55,6 @@ <h3><a href="index.html">Table of Contents</a></h3>
 <li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html#sensitivity-examples">Sensitivity examples</a></li>
 </ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="developer_guide/developer_guide.html">Developer guide</a></li>
-</ul>
 
 <search id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
diff --git a/otbenchmark/master/index.html b/otbenchmark/master/index.html
index b4394365dee..a2fca61078c 100644
--- a/otbenchmark/master/index.html
+++ b/otbenchmark/master/index.html
@@ -60,9 +60,6 @@ <h3><a href="#">Table of Contents</a></h3>
 <li class="toctree-l1"><a class="reference internal" href="user_manual/user_manual.html">API Reference</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
@@ -113,14 +110,6 @@ <h2>User documentation<a class="headerlink" href="#user-documentation" title="Li
 </ul>
 </div>
 </section>
-<section id="developer-guide">
-<h2>Developer guide<a class="headerlink" href="#developer-guide" title="Link to this heading">¶</a></h2>
-<div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="developer_guide/developer_guide.html">Developer guide</a></li>
-</ul>
-</div>
-</section>
 </section>
 <section id="indices-and-tables">
 <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading">¶</a></h1>
diff --git a/otbenchmark/master/objects.inv b/otbenchmark/master/objects.inv
index 974020ae982..b52eaf1eadb 100644
Binary files a/otbenchmark/master/objects.inv and b/otbenchmark/master/objects.inv differ
diff --git a/otbenchmark/master/search.html b/otbenchmark/master/search.html
index fb16aa5a0d2..55bf9e4ed7e 100644
--- a/otbenchmark/master/search.html
+++ b/otbenchmark/master/search.html
@@ -61,9 +61,6 @@ <h3><a href="index.html">Table of Contents</a></h3>
 <li class="toctree-l1"><a class="reference internal" href="user_manual/user_manual.html">API Reference</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
         </div>
diff --git a/otbenchmark/master/searchindex.js b/otbenchmark/master/searchindex.js
index 0451df64348..edf2ba3dbf7 100644
--- a/otbenchmark/master/searchindex.js
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"probabilitysimulationalgorithmfactori": 46, "probabl": 3, "problem": [3, 6, 21, 86], "product": 14, "quasi": 8, "refer": 86, "reliabilitybenchmarkmetaalgorithm": 47, "reliabilitybenchmarkproblem": 48, "reliabilitybenchmarkproblemlist": 53, "reliabilitybenchmarkresult": 49, "reliabilitylibrari": [50, 51, 52, 53], "reliabilityproblem107": 54, "reliabilityproblem110": 55, "reliabilityproblem111": 56, "reliabilityproblem14": 57, "reliabilityproblem22": 58, "reliabilityproblem24": 59, "reliabilityproblem25": 60, "reliabilityproblem28": 61, "reliabilityproblem31": 62, "reliabilityproblem33": 63, "reliabilityproblem35": 64, "reliabilityproblem38": 65, "reliabilityproblem53": 66, "reliabilityproblem54": 67, "reliabilityproblem55": 68, "reliabilityproblem57": 69, "reliabilityproblem60": 70, "reliabilityproblem63": 71, "reliabilityproblem75": 72, "reliabilityproblem77": 73, "reliabilityproblem8": 74, "reliabilityproblem89": 75, "reliabilityproblem91": 76, "reliabl": [3, 6, 86], "rminussreli": 77, "sa": 16, "saltelli": 8, "sampl": 8, "sensit": [0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 86], "sensitivitybenchmarkmetaalgorithm": 79, "sensitivitybenchmarkproblem": 80, "sensitivitybenchmarkproblemlist": 83, "sensitivityconverg": 81, "sensitivitydistribut": 82, "sensitivitylibrari": 83, "sobol": 16, "sorm": [6, 78], "sparsepolynomialchaossensitivityanalysi": 84, "subsetsampl": [6, 85], "sum": 15, "tabl": 24, "test": [9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20], "three": 1, "time": [22, 23, 25], "user": 24, "valu": 5}})
\ No newline at end of file
diff --git a/otbenchmark/master/sg_execution_times.html b/otbenchmark/master/sg_execution_times.html
index c6f50e1b6bd..c9f06ea2e87 100644
--- a/otbenchmark/master/sg_execution_times.html
+++ b/otbenchmark/master/sg_execution_times.html
@@ -56,9 +56,6 @@ <h3><a href="index.html">Table of Contents</a></h3>
 <li class="toctree-l1"><a class="reference internal" href="user_manual/user_manual.html">API Reference</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div role="note" aria-label="source link">
@@ -88,7 +85,7 @@ <h3 id="searchlabel">Quick search</h3>
             
   <section id="computation-times">
 <span id="sphx-glr-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">¶</a></h1>
-<p><strong>01:09.335</strong> total execution time for 8 files <strong>from all galleries</strong>:</p>
+<p><strong>01:46.932</strong> total execution time for 20 files <strong>from all galleries</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -110,35 +107,83 @@ <h3 id="searchlabel">Quick search</h3>
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_convergence_ishigami.html#sphx-glr-auto-examples-sensitivity-problems-plot-convergence-ishigami-py"><span class="std std-ref">Convergence of estimators on Ishigami</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_convergence_ishigami.py</span></code>)</p></td>
-<td><p>00:44.837</p></td>
+<td><p>00:45.054</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/plot_crosscut_distribution_3d.html#sphx-glr-auto-examples-plot-crosscut-distribution-3d-py"><span class="std std-ref">Cross-cuts of conditional distributions in 3-d</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/plot_crosscut_distribution_3d.py</span></code>)</p></td>
-<td><p>00:08.366</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_morris_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-morris-sensitivity-py"><span class="std std-ref">Benchmark the Morris test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_morris_sensitivity.py</span></code>)</p></td>
+<td><p>00:16.944</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/plot_ConditionalDistribution_Demo.html#sphx-glr-auto-examples-plot-conditionaldistribution-demo-py"><span class="std std-ref">Conditional distributions</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/plot_ConditionalDistribution_Demo.py</span></code>)</p></td>
-<td><p>00:04.619</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/plot_crosscut_distribution_3d.html#sphx-glr-auto-examples-plot-crosscut-distribution-3d-py"><span class="std std-ref">Cross-cuts of conditional distributions in 3-d</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/plot_crosscut_distribution_3d.py</span></code>)</p></td>
+<td><p>00:07.724</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/plot_methodFactory.html#sphx-glr-auto-examples-plot-methodfactory-py"><span class="std std-ref">Demonstration of the Factory classes for reliability problems</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/plot_methodFactory.py</span></code>)</p></td>
-<td><p>00:04.439</p></td>
+<td><p>00:07.465</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_nloscillator_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-nloscillator-sensitivity-py"><span class="std std-ref">Benchmark the NLOscillator test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_nloscillator_sensitivity.py</span></code>)</p></td>
+<td><p>00:03.807</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_borehole_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-borehole-sensitivity-py"><span class="std std-ref">Benchmark the Borehole test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_borehole_sensitivity.py</span></code>)</p></td>
+<td><p>00:03.696</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/plot_ConditionalDistribution_Demo.html#sphx-glr-auto-examples-plot-conditionaldistribution-demo-py"><span class="std std-ref">Conditional distributions</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/plot_ConditionalDistribution_Demo.py</span></code>)</p></td>
+<td><p>00:02.577</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_flood_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-flood-sensitivity-py"><span class="std std-ref">Benchmark the Flooding test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_flood_sensitivity.py</span></code>)</p></td>
+<td><p>00:02.451</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_gsobol_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-gsobol-sensitivity-py"><span class="std std-ref">Benchmark the G-Sobol test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_gsobol_sensitivity.py</span></code>)</p></td>
+<td><p>00:02.334</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/plot_crosscut_distribution_2d.html#sphx-glr-auto-examples-plot-crosscut-distribution-2d-py"><span class="std std-ref">Cross-cuts of conditional distributions in 2-d</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/plot_crosscut_distribution_2d.py</span></code>)</p></td>
-<td><p>00:03.997</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_dirichlet_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-dirichlet-sensitivity-py"><span class="std std-ref">Benchmark the Dirichlet test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_dirichlet_sensitivity.py</span></code>)</p></td>
+<td><p>00:02.273</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/plot_convergence_reliability_mc.html#sphx-glr-auto-examples-plot-convergence-reliability-mc-py"><span class="std std-ref">Convergence of Monte-Carlo to estimate the probability in a reliability problem</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/plot_convergence_reliability_mc.py</span></code>)</p></td>
-<td><p>00:02.174</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/plot_convergence_reliability_mc.html#sphx-glr-auto-examples-plot-convergence-reliability-mc-py"><span class="std std-ref">Convergence of Monte-Carlo to estimate the probability in a reliability problem</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/plot_convergence_reliability_mc.py</span></code>)</p></td>
+<td><p>00:02.177</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/plot_crosscut_distribution_2d.html#sphx-glr-auto-examples-plot-crosscut-distribution-2d-py"><span class="std std-ref">Cross-cuts of conditional distributions in 2-d</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/plot_crosscut_distribution_2d.py</span></code>)</p></td>
+<td><p>00:02.066</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_gaussian_sum.html#sphx-glr-auto-examples-sensitivity-problems-plot-gaussian-sum-py"><span class="std std-ref">Benchmark the gaussian sum test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_gaussian_sum.py</span></code>)</p></td>
+<td><p>00:01.770</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_gaussian_product_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-gaussian-product-sensitivity-py"><span class="std std-ref">Benchmark the gaussian product test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_gaussian_product_sensitivity.py</span></code>)</p></td>
+<td><p>00:01.658</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="auto_examples/plot_CrossCutFunction_Demo.html#sphx-glr-auto-examples-plot-crosscutfunction-demo-py"><span class="std std-ref">Draw cross-cuts of multidimensional functions</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/plot_CrossCutFunction_Demo.py</span></code>)</p></td>
-<td><p>00:00.738</p></td>
+<td><p>00:01.229</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_ishigami_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-ishigami-sensitivity-py"><span class="std std-ref">Benchmark the Ishigami test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_ishigami_sensitivity.py</span></code>)</p></td>
+<td><p>00:01.187</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_borgonovo_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-borgonovo-sensitivity-py"><span class="std std-ref">Benchmark the Borgonovo test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_borgonovo_sensitivity.py</span></code>)</p></td>
+<td><p>00:01.145</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_oakleyohagan_sensitivity.html#sphx-glr-auto-examples-sensitivity-problems-plot-oakleyohagan-sensitivity-py"><span class="std std-ref">Benchmark the Oakley-O’Hagan test function</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_oakleyohagan_sensitivity.py</span></code>)</p></td>
+<td><p>00:01.088</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_print_problems.html#sphx-glr-auto-examples-sensitivity-problems-plot-print-problems-py"><span class="std std-ref">Print the sensitivity analysis problems</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_print_problems.py</span></code>)</p></td>
+<td><p>00:00.252</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_print_problems.html#sphx-glr-auto-examples-sensitivity-problems-plot-print-problems-py"><span class="std std-ref">Print the sensitivity analysis problems</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_print_problems.py</span></code>)</p></td>
-<td><p>00:00.165</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/sensitivity_problems/plot_benchmark_sensitivity_methods.html#sphx-glr-auto-examples-sensitivity-problems-plot-benchmark-sensitivity-methods-py"><span class="std std-ref">Benchmark sensitivity analysis methods</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/sensitivity_problems/plot_benchmark_sensitivity_methods.py</span></code>)</p></td>
+<td><p>00:00.034</p></td>
 <td><p>0.0</p></td>
 </tr>
 </tbody>
diff --git a/otbenchmark/master/user_manual/_generated/otbenchmark.AxialStressedBeamReliability.AxialStressedBeamReliability.html b/otbenchmark/master/user_manual/_generated/otbenchmark.AxialStressedBeamReliability.AxialStressedBeamReliability.html
index 2bf65c3234b..281489467e3 100644
--- a/otbenchmark/master/user_manual/_generated/otbenchmark.AxialStressedBeamReliability.AxialStressedBeamReliability.html
+++ b/otbenchmark/master/user_manual/_generated/otbenchmark.AxialStressedBeamReliability.AxialStressedBeamReliability.html
@@ -71,9 +71,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
diff --git a/otbenchmark/master/user_manual/_generated/otbenchmark.BoreholeSensitivity.BoreholeSensitivity.html b/otbenchmark/master/user_manual/_generated/otbenchmark.BoreholeSensitivity.BoreholeSensitivity.html
index 6a82c93026f..6be4acc18c8 100644
--- a/otbenchmark/master/user_manual/_generated/otbenchmark.BoreholeSensitivity.BoreholeSensitivity.html
+++ b/otbenchmark/master/user_manual/_generated/otbenchmark.BoreholeSensitivity.BoreholeSensitivity.html
@@ -71,9 +71,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
diff --git a/otbenchmark/master/user_manual/_generated/otbenchmark.BorgonovoSensitivity.BorgonovoSensitivity.html b/otbenchmark/master/user_manual/_generated/otbenchmark.BorgonovoSensitivity.BorgonovoSensitivity.html
index ecb029768f4..04e79f0caf1 100644
--- a/otbenchmark/master/user_manual/_generated/otbenchmark.BorgonovoSensitivity.BorgonovoSensitivity.html
+++ b/otbenchmark/master/user_manual/_generated/otbenchmark.BorgonovoSensitivity.BorgonovoSensitivity.html
@@ -71,9 +71,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
diff --git a/otbenchmark/master/user_manual/_generated/otbenchmark.ConditionalDistribution.ConditionalDistribution.html b/otbenchmark/master/user_manual/_generated/otbenchmark.ConditionalDistribution.ConditionalDistribution.html
index 4375a7da32a..8f27d2c2e1b 100644
--- a/otbenchmark/master/user_manual/_generated/otbenchmark.ConditionalDistribution.ConditionalDistribution.html
+++ b/otbenchmark/master/user_manual/_generated/otbenchmark.ConditionalDistribution.ConditionalDistribution.html
@@ -71,9 +71,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
diff --git a/otbenchmark/master/user_manual/_generated/otbenchmark.CrossCutDistribution.CrossCutDistribution.html b/otbenchmark/master/user_manual/_generated/otbenchmark.CrossCutDistribution.CrossCutDistribution.html
index 4171fe37bad..c6cfa1aee0f 100644
--- a/otbenchmark/master/user_manual/_generated/otbenchmark.CrossCutDistribution.CrossCutDistribution.html
+++ b/otbenchmark/master/user_manual/_generated/otbenchmark.CrossCutDistribution.CrossCutDistribution.html
@@ -71,9 +71,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
diff --git a/otbenchmark/master/user_manual/_generated/otbenchmark.CrossCutFunction.CrossCutFunction.html b/otbenchmark/master/user_manual/_generated/otbenchmark.CrossCutFunction.CrossCutFunction.html
index d95aaef309e..c7e4f7ec834 100644
--- a/otbenchmark/master/user_manual/_generated/otbenchmark.CrossCutFunction.CrossCutFunction.html
+++ b/otbenchmark/master/user_manual/_generated/otbenchmark.CrossCutFunction.CrossCutFunction.html
@@ -71,9 +71,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../auto_examples/index.html#sensitivity-examples">Sensitivity examples</a></li>
-</ul>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../developer_guide/developer_guide.html">Developer guide</a></li>
 </ul>
 
   <div>
diff --git a/otbenchmark/master/user_manual/_generated/otbenchmark.DirichletSensitivity.DirichletSensitivity.html b/otbenchmark/master/user_manual/_generated/otbenchmark.DirichletSensitivity.DirichletSensitivity.html
index f650cfc67ab..ce058824369 100644
--- a/otbenchmark/master/user_manual/_generated/otbenchmark.DirichletSensitivity.DirichletSensitivity.html
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@@ -71,9 +71,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
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@@ -71,9 +71,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
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diff --git a/otbenchmark/master/user_manual/_generated/otbenchmark.SparsePolynomialChaosSensitivityAnalysis.SparsePolynomialChaosSensitivityAnalysis.html b/otbenchmark/master/user_manual/_generated/otbenchmark.SparsePolynomialChaosSensitivityAnalysis.SparsePolynomialChaosSensitivityAnalysis.html
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@@ -71,9 +71,6 @@ <h3><a href="../../index.html">Table of Contents</a></h3>
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