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diff --git a/.nojekyll b/.nojekyll
@@ -1 +1 @@
-57dbfbac
+48d009f4
diff --git a/schedule/slides/06-information-criteria.html b/schedule/slides/06-information-criteria.html
@@ -398,7 +398,7 @@
 <h2>06 Information Criteria</h2>
 <p><span class="secondary">Stat 406</span></p>
 <p><span class="secondary">Geoff Pleiss, Trevor Campbell</span></p>
-<p>Last modified – 23 September 2024</p>
+<p>Last modified – 24 September 2024</p>
 <p><span class="math display">\[
 \DeclareMathOperator*{\argmin}{argmin}
 \DeclareMathOperator*{\argmax}{argmax}
@@ -447,7 +447,7 @@ <h2>LOO-CV: Math to the rescue!</h2>
 <ul>
 <li><span class="math inline">\(\hat{\mathbf y} = \begin{bmatrix} \hat Y_1 &amp; \cdots &amp; \hat Y_n \end{bmatrix}^\top \in \mathbb R^{n}\)</span></li>
 <li><span class="math inline">\({\mathbf y} = \begin{bmatrix} Y_1 &amp; \cdots &amp; Y_n \end{bmatrix}^\top \in \mathbb R^{n}\)</span></li>
-<li><span class="math inline">\(\mathbf H = \begin{bmatrix} \mathbf h_1(X_1) &amp; \cdots &amp; \mathbf h_n(X_n) \end{bmatrix}^\top \in \mathbb R^{n \times n}\)</span></li>
+<li><span class="math inline">\(\mathbf H = \begin{bmatrix} \mathbf h_1(X_{1:n}) &amp; \cdots &amp; \mathbf h_n(X_{1:n}) \end{bmatrix}^\top \in \mathbb R^{n \times n}\)</span></li>
 </ul>
 <div class="fragment">
 <p>For example, OLS:</p>

diff --git a/search.json b/search.json
@@ -3105,7 +3105,7 @@
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     "title": "UBC Stat406 2024W",
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-    "text": "06 Information Criteria\nStat 406\nGeoff Pleiss, Trevor Campbell\nLast modified – 23 September 2024\n\\[\n\\DeclareMathOperator*{\\argmin}{argmin}\n\\DeclareMathOperator*{\\argmax}{argmax}\n\\DeclareMathOperator*{\\minimize}{minimize}\n\\DeclareMathOperator*{\\maximize}{maximize}\n\\DeclareMathOperator*{\\find}{find}\n\\DeclareMathOperator{\\st}{subject\\,\\,to}\n\\newcommand{\\E}{E}\n\\newcommand{\\Expect}[1]{\\E\\left[ #1 \\right]}\n\\newcommand{\\Var}[1]{\\mathrm{Var}\\left[ #1 \\right]}\n\\newcommand{\\Cov}[2]{\\mathrm{Cov}\\left[#1,\\ #2\\right]}\n\\newcommand{\\given}{\\ \\vert\\ }\n\\newcommand{\\X}{\\mathbf{X}}\n\\newcommand{\\x}{\\mathbf{x}}\n\\newcommand{\\y}{\\mathbf{y}}\n\\newcommand{\\P}{\\mathcal{P}}\n\\newcommand{\\R}{\\mathbb{R}}\n\\newcommand{\\norm}[1]{\\left\\lVert #1 \\right\\rVert}\n\\newcommand{\\snorm}[1]{\\lVert #1 \\rVert}\n\\newcommand{\\tr}[1]{\\mbox{tr}(#1)}\n\\newcommand{\\brt}{\\widehat{\\beta}^R_{s}}\n\\newcommand{\\brl}{\\widehat{\\beta}^R_{\\lambda}}\n\\newcommand{\\bls}{\\widehat{\\beta}_{ols}}\n\\newcommand{\\blt}{\\widehat{\\beta}^L_{s}}\n\\newcommand{\\bll}{\\widehat{\\beta}^L_{\\lambda}}\n\\newcommand{\\U}{\\mathbf{U}}\n\\newcommand{\\D}{\\mathbf{D}}\n\\newcommand{\\V}{\\mathbf{V}}\n\\]"
+    "text": "06 Information Criteria\nStat 406\nGeoff Pleiss, Trevor Campbell\nLast modified – 24 September 2024\n\\[\n\\DeclareMathOperator*{\\argmin}{argmin}\n\\DeclareMathOperator*{\\argmax}{argmax}\n\\DeclareMathOperator*{\\minimize}{minimize}\n\\DeclareMathOperator*{\\maximize}{maximize}\n\\DeclareMathOperator*{\\find}{find}\n\\DeclareMathOperator{\\st}{subject\\,\\,to}\n\\newcommand{\\E}{E}\n\\newcommand{\\Expect}[1]{\\E\\left[ #1 \\right]}\n\\newcommand{\\Var}[1]{\\mathrm{Var}\\left[ #1 \\right]}\n\\newcommand{\\Cov}[2]{\\mathrm{Cov}\\left[#1,\\ #2\\right]}\n\\newcommand{\\given}{\\ \\vert\\ }\n\\newcommand{\\X}{\\mathbf{X}}\n\\newcommand{\\x}{\\mathbf{x}}\n\\newcommand{\\y}{\\mathbf{y}}\n\\newcommand{\\P}{\\mathcal{P}}\n\\newcommand{\\R}{\\mathbb{R}}\n\\newcommand{\\norm}[1]{\\left\\lVert #1 \\right\\rVert}\n\\newcommand{\\snorm}[1]{\\lVert #1 \\rVert}\n\\newcommand{\\tr}[1]{\\mbox{tr}(#1)}\n\\newcommand{\\brt}{\\widehat{\\beta}^R_{s}}\n\\newcommand{\\brl}{\\widehat{\\beta}^R_{\\lambda}}\n\\newcommand{\\bls}{\\widehat{\\beta}_{ols}}\n\\newcommand{\\blt}{\\widehat{\\beta}^L_{s}}\n\\newcommand{\\bll}{\\widehat{\\beta}^L_{\\lambda}}\n\\newcommand{\\U}{\\mathbf{U}}\n\\newcommand{\\D}{\\mathbf{D}}\n\\newcommand{\\V}{\\mathbf{V}}\n\\]"
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@@ -3119,7 +3119,7 @@
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     "title": "UBC Stat406 2024W",
     "section": "LOO-CV: Math to the rescue!",
-    "text": "LOO-CV: Math to the rescue!\nConsider models where predictions are a linear function of the training responses, i.e.,\n\\[ \\hat{\\mathbf y} = {\\mathbf H} {\\mathbf y} \\]\nwhere we collected terms into matrices and vectors (\\({\\mathbf h_i}\\) can be any functions):\n\n\\(\\hat{\\mathbf y} = \\begin{bmatrix} \\hat Y_1 & \\cdots & \\hat Y_n \\end{bmatrix}^\\top \\in \\mathbb R^{n}\\)\n\\({\\mathbf y} = \\begin{bmatrix} Y_1 & \\cdots & Y_n \\end{bmatrix}^\\top \\in \\mathbb R^{n}\\)\n\\(\\mathbf H = \\begin{bmatrix} \\mathbf h_1(X_1) & \\cdots & \\mathbf h_n(X_n) \\end{bmatrix}^\\top \\in \\mathbb R^{n \\times n}\\)\n\n\nFor example, OLS:\n\\[ \\hat{\\mathbf y} = {\\mathbf X} \\hat \\beta, \\qquad \\hat\\beta = (\\mathbf X^\\top \\mathbf X)^{-1} \\mathbf X^\\top \\mathbf y \\]\nBy inspection \\(\\mathbf H = \\mathbf X (\\mathbf X^\\top \\mathbf X)^{-1} \\mathbf X^\\top\\)"
+    "text": "LOO-CV: Math to the rescue!\nConsider models where predictions are a linear function of the training responses, i.e.,\n\\[ \\hat{\\mathbf y} = {\\mathbf H} {\\mathbf y} \\]\nwhere we collected terms into matrices and vectors (\\({\\mathbf h_i}\\) can be any functions):\n\n\\(\\hat{\\mathbf y} = \\begin{bmatrix} \\hat Y_1 & \\cdots & \\hat Y_n \\end{bmatrix}^\\top \\in \\mathbb R^{n}\\)\n\\({\\mathbf y} = \\begin{bmatrix} Y_1 & \\cdots & Y_n \\end{bmatrix}^\\top \\in \\mathbb R^{n}\\)\n\\(\\mathbf H = \\begin{bmatrix} \\mathbf h_1(X_{1:n}) & \\cdots & \\mathbf h_n(X_{1:n}) \\end{bmatrix}^\\top \\in \\mathbb R^{n \\times n}\\)\n\n\nFor example, OLS:\n\\[ \\hat{\\mathbf y} = {\\mathbf X} \\hat \\beta, \\qquad \\hat\\beta = (\\mathbf X^\\top \\mathbf X)^{-1} \\mathbf X^\\top \\mathbf y \\]\nBy inspection \\(\\mathbf H = \\mathbf X (\\mathbf X^\\top \\mathbf X)^{-1} \\mathbf X^\\top\\)"
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