diff --git a/applications/train_classifier_ptype.py b/applications/train_classifier_ptype.py
index c5391dd..5ec356a 100644
--- a/applications/train_classifier_ptype.py
+++ b/applications/train_classifier_ptype.py
@@ -83,7 +83,7 @@ def trainer(conf, evaluate=True, data_split=0, mc_forward_passes=0):
     output_features = conf["output_features"]
     metric = conf["metric"]
     # flag for using the evidential model
-    if conf["model"]["loss"] == "dirichlet":
+    if conf["model"]["loss"] == "evidential":
         use_uncertainty = True
     else:
         use_uncertainty = False
@@ -177,6 +177,10 @@ def trainer(conf, evaluate=True, data_split=0, mc_forward_passes=0):
             x = scaled_data[f"{name}_x"]
             if use_uncertainty:
                 pred_probs, u, ale, epi = mlp.predict(x, return_uncertainties=True)
+                pred_probs = pred_probs.numpy()
+                u = u.numpy()
+                ale = ale.numpy()
+                epi = epi.numpy()
                 entropy = np.zeros(pred_probs.shape)
                 mutual_info = np.zeros(pred_probs.shape)
             elif mc_forward_passes > 0:  # Compute epistemic uncertainty with MC dropout
@@ -185,7 +189,7 @@ def trainer(conf, evaluate=True, data_split=0, mc_forward_passes=0):
                     x, mc_forward_passes=mc_forward_passes)
                 u = np.zeros(pred_probs.shape)
             else:
-                pred_probs = mlp.predict(x)
+                pred_probs = mlp.predict(x, return_uncertainties=False)
                 ale = np.zeros(pred_probs.shape)
                 u = np.zeros(pred_probs.shape)
                 epi = np.zeros(pred_probs.shape)
diff --git a/config/ptype/evidential.yml b/config/ptype/evidential.yml
index 7018159..7c52f64 100644
--- a/config/ptype/evidential.yml
+++ b/config/ptype/evidential.yml
@@ -135,7 +135,7 @@ case_studies:
   - '2021-02-17'
   - '2021-02-18'
   - '2021-02-19'
-data_path: /glade/p/cisl/aiml/ai2es/winter_ptypes/ptype_qc/mPING_interpolated_QC2.parquet
+data_path: /glade/campaign/cisl/aiml/ai2es/winter_ptypes/ptype_qc/mPING_hourafter_interpolated_QC3.parquet
 direction: max
 ensemble:
   mc_steps: 0
@@ -147,24 +147,23 @@ input_features:
 - VGRD_m/s
 metric: val_ave_acc
 model:
-  activation: leaky
-  annealing_coeff: 34.593686950910275
-  balanced_classes: 1
-  batch_size: 100
-  dropout_alpha: 0.20146936081973893
-  epochs: 1000
-  hidden_layers: 2
-  hidden_neurons: 6461
+  activation: leaky_relu
+  annealing_coeff: 34
+  batch_size: 1130
+  dropout_alpha: 0.11676011477923032
+  epochs: 100
+  evidential: true
+  n_inputs: 84
+  hidden_layers: 4
+  hidden_neurons: 212
+  l2_weight: 0.000881889591229087
   loss: evidential
-  loss_weights:
-  - 58.64242174310205
-  - 94.59680461256323
-  - 124.5896569779261
-  - 227.38800030539545
-  lr: 0.0027750619126744817
+  lr: 0.004800502096767794
+  n_classes: 4
   optimizer: adam
+  output_activation: linear
   use_dropout: 1
-  verbose: 0
+  verbose: 1
 mping_path: /glade/p/cisl/aiml/ai2es/winter_ptypes/precip_rap/mPING_mixture/
 output_features:
 - ra_percent
diff --git a/config/surface_layer/mlp.yml b/config/surface_layer/mlp.yml
index 84fb6bd..1cf0fec 100644
--- a/config/surface_layer/mlp.yml
+++ b/config/surface_layer/mlp.yml
@@ -42,7 +42,7 @@ model:
     hidden_neurons: 500
     activation: "relu"
     optimizer: "adam"
-    metrics: "mae"
+    # metrics: "mae"
     lr: 0.0004727390951751
     kernel_reg: 'l2'
     l1_weight: 0.0
diff --git a/environment_gpu.yml b/environment_gpu.yml
index 0563423..f733088 100644
--- a/environment_gpu.yml
+++ b/environment_gpu.yml
@@ -3,8 +3,8 @@ channels:
   - conda-forge
   - nvidia
 dependencies:
-  - python=3.11
-  - numpy=1.23.*
+  - python=3.10
+  - numpy<1.24
   - scipy
   - matplotlib
   - xarray
@@ -22,11 +22,12 @@ dependencies:
   - seaborn
   - sphinx
   - numba
-  - imbalanced-learn
-  - pyarrow
   - properscoring
+  - pyarrow
+  - imbalanced-learn
   - pip:
     - tensorflow[and-cuda]
+    - keras
     - echo-opt
     - hagelslag
     - bridgescaler
diff --git a/mlguess/keras/callbacks.py b/mlguess/keras/callbacks.py
index 2f60b4d..55cc0db 100644
--- a/mlguess/keras/callbacks.py
+++ b/mlguess/keras/callbacks.py
@@ -13,9 +13,12 @@
 import os
 import keras
 import numpy as np
+from hagelslag.evaluation.ProbabilityMetrics import DistributedROC
+from sklearn.metrics import precision_recall_fscore_support, roc_auc_score
 
 logger = logging.getLogger(__name__)
 
+
 def get_callbacks(config: Dict[str, str], path_extend=False) -> List[Callback]:
     callbacks = []
 
@@ -72,13 +75,17 @@ def on_epoch_end(self, epoch: int, logs: Dict[str, float] = None) -> None:
         logs = logs or {}
         logs["lr"] = K.get_value(self.model.optimizer.lr)
 
-
+@keras.saving.register_keras_serializable()
 class ReportEpoch(keras.callbacks.Callback):
     def __init__(self, epoch_var):
         self.epoch_var = epoch_var
 
-    def on_epoch_begin(self, epoch, logs=None):
-        self.epoch_var.assign_add(1)
+    def on_epoch_end(self, epoch, logs={}):
+        self.epoch_var += 1
+
+    def get_config(self):
+
+        return {}
 
 
 class MetricsCallback(keras.callbacks.Callback):
@@ -94,10 +101,11 @@ def __init__(self, x, y, name="val", n_bins=10, use_uncertainty=False, **kwargs)
         self.bin_uppers = bin_boundaries[1:]
 
     def on_epoch_end(self, epoch, logs={}):
-        pred_probs = np.asarray(self.model.predict(self.x))
         if self.use_uncertainty:
-            pred_probs, _, _, _ = calc_prob_uncertainty(pred_probs)
+            pred_probs, _, _, _ = self.model.predict(self.x, return_uncertainties=True)
             pred_probs = pred_probs.numpy()
+        else:
+            pred_probs = np.asarray(self.model.predict(self.x, return_uncertainties=False))
         logs[f"{self.name}_csi"] = self.mean_csi(pred_probs)
         true_labels = np.argmax(self.y, 1)
         pred_labels = np.argmax(pred_probs, 1)
@@ -187,4 +195,3 @@ def ece(self, true_labels, pred_probs):
                     pass
         mean = np.mean(ece) if np.isfinite(np.mean(ece)) else self.bin_lowers.shape[0]
         return mean
-
diff --git a/mlguess/keras/layers.py b/mlguess/keras/layers.py
index 13f2b4d..7bf018b 100644
--- a/mlguess/keras/layers.py
+++ b/mlguess/keras/layers.py
@@ -1,6 +1,8 @@
 import keras
 import keras.layers as layers
 import keras.ops as ops
+from keras.src import activations
+from keras.src.layers.layer import Layer
 
 
 @keras.saving.register_keras_serializable()
diff --git a/mlguess/keras/losses.py b/mlguess/keras/losses.py
index fcc3d7a..bdc96a6 100644
--- a/mlguess/keras/losses.py
+++ b/mlguess/keras/losses.py
@@ -95,6 +95,7 @@ def loss(y, y_pred):
 
     return loss
 
+@keras.saving.register_keras_serializable()
 def gaussian_nll(y, y_pred, reduce=True):
     """
     Loss function for a parametric Gaussian Loss.
diff --git a/mlguess/keras/models.py b/mlguess/keras/models.py
index 0935fa3..304bb05 100644
--- a/mlguess/keras/models.py
+++ b/mlguess/keras/models.py
@@ -1,17 +1,27 @@
+import sys
 import keras
 import keras.ops as ops
 import numpy as np
 from keras.regularizers import L1, L2, L1L2
-from keras.layers import Dense, LeakyReLU, GaussianNoise, Dropout
-from mlguess.keras.layers import DenseNormalGamma
-import sys
+from keras.layers import Dense, GaussianNoise, Dropout
+from mlguess.keras.layers import DenseNormalGamma, DenseNormal
+from mlguess.keras.losses import evidential_cat_loss, evidential_reg_loss, gaussian_nll
+from mlguess.keras.callbacks import ReportEpoch
+from keras.optimizers import Adam, SGD
+
 
+
+@keras.saving.register_keras_serializable()
 class CategoricalDNN(keras.models.Model):
     """
-    A Dense Neural Network Model that can support arbitrary numbers of hidden layers.
+    A Categorical Dense Neural Network Model that can support arbitrary numbers of hidden layers
+    and the ability to provide evidential uncertainty estimation.
+
     Attributes:
         hidden_layers: Number of hidden layers
         hidden_neurons: Number of neurons in each hidden layer
+        evidential (bool): Whether to use evidential approach (custom loss)
+        annealing_coeff: Annealing coefficient for evidential loss (ignored if evidential==False)
         activation: Type of activation function
         output_activation: Activation function applied to the output layer
         optimizer: Name of optimizer or optimizer object.
@@ -31,16 +41,56 @@ class CategoricalDNN(keras.models.Model):
         decay: Level of decay to apply to learning rate
         verbose: Level of detail to provide during training (0 = None, 1 = Minimal, 2 = All)
         classifier: (boolean) If training on classes
+
+        Example:
+
+            When evidential==True, the output activation and the loss function will be overridden under the hood. When
+            evidential==False, it will use the parameters specified and ignore the annealing_coeff.
+            Note: Model compilation happens under the hood when .fit() is called.
+
+            n_samples = 1000
+            n_features = 23
+            n_classes = 5
+            x_train = np.random.random(size=(n_samples, n_features))
+            y_train = np.random.randint(low=0, high=n_classes, size=n_samples)
+
+            ### Evidential
+            model = CategoricalDNN(hidden_layers=2,
+                                   evidential=True,
+                                   activation='relu',
+                                   n_classes=n_classes,
+                                   n_inputs=n_features,
+                                   epochs=10,
+                                   annealing_coeff=1.5,
+                                   lr=0.0001)
+            hist = model.fit(x_train, y_train)
+            p_with_uncertainty = model.predict(x_train, return_uncertainties=True, batch_size=5000)
+
+            ### Vanilla DNN
+            model = CategoricalDNN(hidden_layers=2,
+                                   evidential=False,
+                                   activation='relu',
+                                   output_activation='softmax',
+                                   loss='categorical_crossentropy',
+                                   n_classes=n_classes,
+                                   n_inputs=n_features,
+                                   epochs=10,
+                                   lr=0.0001)
+            hist = model.fit(x_train, y_train)
+            p = model.predict(x_train, return_uncertainties=False, batch_size=5000)
     """
-    def __init__(self, hidden_layers=2, hidden_neurons=64, activation="relu", output_activation="softmax",
-                 optimizer="adam", loss="categorical_crossentropy",loss_weights=None, annealing_coeff=1.0,
-                 use_noise=False, noise_sd=0.0, lr=0.001, use_dropout=False, dropout_alpha=0.2, batch_size=128,
-                 epochs=2, kernel_reg=None, l1_weight=0.0, l2_weight=0.0, sgd_momentum=0.9, adam_beta_1=0.9,
-                 adam_beta_2=0.999, epsilon=1e-7, decay=0, verbose=0, random_state=1000, callbacks=None,
-                 balanced_classes=0, steps_per_epoch=0, n_classes=2, **kwargs):
+
+    def __init__(self, hidden_layers=2, hidden_neurons=64, evidential=False, activation="relu",
+                 output_activation="softmax", optimizer="adam", loss="categorical_crossentropy", loss_weights=None,
+                 annealing_coeff=1.0, use_noise=False, noise_sd=0.0, lr=0.001, use_dropout=False, dropout_alpha=0.2,
+                 batch_size=128, epochs=2, kernel_reg=None, l1_weight=0.0, l2_weight=0.0, sgd_momentum=0.9,
+                 adam_beta_1=0.9, adam_beta_2=0.999, epsilon=1e-7, decay=0, verbose=0, random_state=1000, n_classes=2,
+                 n_inputs=42, callbacks=[], **kwargs):
+
         super().__init__(**kwargs)
         self.hidden_layers = hidden_layers
         self.hidden_neurons = hidden_neurons
+        self.evidential = evidential
         self.activation = activation
         self.output_activation = output_activation
         self.optimizer = optimizer
@@ -62,27 +112,24 @@ def __init__(self, hidden_layers=2, hidden_neurons=64, activation="relu", output
         self.use_dropout = use_dropout
         self.dropout_alpha = dropout_alpha
         self.epochs = epochs
-        if callbacks is None:
-            self.callbacks = []
-        else:
-            self.callbacks = callbacks
         self.decay = decay
         self.verbose = verbose
         self.random_state = random_state
         self.n_classes = n_classes
-        self.hyperparameters = ["hidden_layers", "hidden_neurons", "activation", "output_activation",
+        self.n_inputs = n_inputs
+        self.callbacks = callbacks
+        self.hyperparameters = ["hidden_layers", "hidden_neurons", "evidential", "activation", "output_activation",
                                 "optimizer", "sgd_momentum", "adam_beta_1", "adam_beta_2", "epsilon", "loss",
                                 "loss_weights", "annealing_coeff", "lr", "kernel_reg", "l1_weight", "l2_weight",
                                 "batch_size", "use_noise", "noise_sd", "use_dropout", "dropout_alpha", "epochs",
-                                "callbacks", "decay", "verbose", "random_state", "n_classes"]
+                                "callbacks", "decay", "verbose", "random_state", "n_classes", "n_inputs"]
         """
         Create Keras neural network model and compile it.
         Args:
             inputs (int): Number of input predictor variables
             outputs (int): Number of output predictor variables
         """
-        if self.activation == "leaky":
-            self.activation = LeakyReLU()
+
         if self.kernel_reg == "l1":
             self.kernel_reg = L1(self.l1_weight)
         elif self.kernel_reg == "l2":
@@ -91,36 +138,77 @@ def __init__(self, hidden_layers=2, hidden_neurons=64, activation="relu", output
             self.kernel_reg = L1L2(self.l1_weight, self.l2_weight)
         else:
             self.kernel_reg = None
+
+        if self.optimizer == "adam":
+            self.optimizer_obj = Adam(learning_rate=self.lr,
+                                      beta_1=self.adam_beta_1,
+                                      beta_2=self.adam_beta_2,
+                                      epsilon=self.epsilon)
+        elif self.optimizer == "sgd":
+            self.optimizer_obj = SGD(learning_rate=self.lr, momentum=self.sgd_momentum)
+
+        if self.evidential:
+            self.output_activation = "linear"
+
         self.model_layers = []
+        self.model_layers.append(Dense(self.n_inputs,
+                                       activation=self.activation,
+                                       kernel_regularizer=self.kernel_reg,
+                                       name="input_dense"))
         for h in range(self.hidden_layers):
             self.model_layers.append(Dense(self.hidden_neurons,
-                                                       activation=self.activation,
-                                                       kernel_regularizer=self.kernel_reg,
-                                                       name=f"dense_{h:02d}"))
+                                           activation=self.activation,
+                                           kernel_regularizer=self.kernel_reg,
+                                           name=f"dense_{h:02d}"))
             if self.use_dropout:
                 self.model_layers.append(Dropout(self.dropout_alpha, name=f"dropout_{h:02d}"))
             if self.use_noise:
                 self.model_layers.append(GaussianNoise(self.noise_sd, name=f"noise_{h:02d}"))
 
-        self.model_layers.append(Dense(self.n_classes, activation=self.output_activation, name="dense_output"))
+        self.model_layers.append(Dense(self.n_classes,
+                                       activation=self.output_activation,
+                                       name="dense_output"))
 
     def call(self, inputs):
 
-        layer_output = self.model_layers[0](inputs)
-
+        mod = self.model_layers[0](inputs)
         for l in range(1, len(self.model_layers)):
-            layer_output = self.model_layers[l](layer_output)
+            mod = self.model_layers[l](mod)
 
-        return layer_output
+        return mod
 
     def fit(self, x=None, y=None, **kwargs):
 
-        hist = super().fit(x, y, **kwargs)
+        if self.evidential:
+            e = keras.Variable(1)
+            report_epoch_callback = ReportEpoch(e)
+            self.loss = evidential_cat_loss(evi_coef=self.annealing_coeff,
+                                            epoch_callback=report_epoch_callback)
+            self.callbacks.append(report_epoch_callback)
+
+        super().compile(loss=self.loss,
+                        optimizer=self.optimizer_obj,
+                        run_eagerly=False)
+
+        hist = super().fit(x, y, epochs=self.epochs, batch_size=self.batch_size,
+                           callbacks=self.callbacks, **kwargs)
 
         return hist
 
     def predict(self, x, return_uncertainties=True, **kwargs):
-
+        """
+        Args:
+            x: Input data
+            batch_size: Size of batch to predict
+            return_uncertainties: Returns derived uncertainties from evidential distribution parameters.
+                                  If False, return the probabilities only.
+        Returns:
+            If return_uncertainties is True (tuple): (probs, u (evidential uncertainty), aleatoric, epistemic)
+            Else If return_uncertainties is False: probs
+        """
+        if (not self.evidential) and return_uncertainties:
+            raise NotImplementedError("You can only return uncertainty estimates when 'evidential' is True. Otherwise "
+                                      "you can set 'return_uncertainties' to False to return probabilities.")
         output = super().predict(x, **kwargs)
         if return_uncertainties:
             return self.calc_uncertainty(output)
@@ -135,7 +223,7 @@ def calc_uncertainty(self, y_pred):
         u = num_classes / S
         prob = alpha / S
         epistemic = prob * (1 - prob) / (S + 1)
-        aleatoric = prob - prob**2 - epistemic
+        aleatoric = prob - prob ** 2 - epistemic
         return prob, u, aleatoric, epistemic
 
     def predict_dropout(self, x, mc_forward_passes=10, batch_size=None):
@@ -169,19 +257,22 @@ def predict_dropout(self, x, mc_forward_passes=10, batch_size=None):
     def get_config(self):
         base_config = super().get_config()
         parameter_config = {hp: getattr(self, hp) for hp in self.hyperparameters}
+        parameter_config['callbacks'] = []
         return {**base_config, **parameter_config}
 
 
-class EvidentialRegressorDNN(keras.models.Model):
+class RegressorDNN(keras.models.Model):
     """
     A Dense Neural Network Model that can support arbitrary numbers of hidden layers
-    and provides evidential uncertainty estimation.
-    Inherits from BaseRegressor.
+    and the ability to provide evidential uncertainty estimation or uncertainty estimation through
+    a gaussian parametric approach.
 
     Attributes:
         hidden_layers: Number of hidden layers.
         hidden_neurons: Number of neurons in each hidden layer.
         activation: Type of activation function.
+        evidential (bool): Whether to use evidential model (outputs [mean, aleatoric, epistemic])
+        uncertainty (bool): Whether to use the Gaussian Parametric approach (outputs [mean, std])
         optimizer: Name of optimizer or optimizer object.
         loss: Name of loss function or loss object.
         use_noise: Whether additive Gaussian noise layers are included in the network.
@@ -191,18 +282,64 @@ class EvidentialRegressorDNN(keras.models.Model):
         batch_size: Number of examples per batch.
         epochs: Number of epochs to train.
         verbose: Level of detail to provide during training.
-        model: Keras Model object.
-        evidential_coef: Evidential regularization coefficient.
+        evi_coeff: Evidential regularization coefficient.
         metrics: Optional list of metrics to monitor during training.
+
+        Example:
+
+            When evidential==True or uncertainty==True, the output activation and the loss function will be overridden
+            under the hood. If both are True, the evidential model will override. When both are set to False,
+            it will train a generic DNN with a linear output activation and the specified loss function.
+            'evi_coeff' is only used when evidential==True and is otherwise ignored.
+            Note: Model compilation happens under the hood when .fit() is called.
+
+            n_samples = 1000
+            n_features = 23
+            n_output_tasks = 1
+            x_train = np.random.random(size=(n_samples, n_features))
+            y_train = np.random.random(size=(n_samples, n_output_tasks)
+
+            ### Evidential
+            model = RegressorDNN(hidden_layers=2,
+                                 n_output_tasks=n_output_tasks,
+                                 n_inputs=n_features,
+                                 evidential=True,
+                                 epochs=10)
+            model.fit(x_train, y_train)
+            p_with_uncertainty = model.predict(x_train, return_uncertainties=True)
+
+
+            ### Gaussian Parametric
+            model = RegressorDNN(hidden_layers=2,
+                                 n_output_tasks=n_output_tasks,
+                                 n_inputs=n_features,
+                                 evidential=False,
+                                 uncertainty=True,
+                                 epochs=10)
+            model.fit(x_train, y_train)
+            p_with_uncertainty = model.predict(x_train, return_uncertainties=True)
+
+
+            ### Vanilla DNN
+            model = RegressorDNN(hidden_layers=2,
+                                 n_output_tasks=n_output_tasks,
+                                 n_inputs=n_features,
+                                 evidential=False,
+                                 uncertainty=False,
+                                 epochs=10)
+            model.fit(x_train, y_train)
+            p = model.predict(x_train, return_uncertainties=False)
     """
-    def __init__(self, hidden_layers=2, hidden_neurons=64, activation="relu", optimizer="adam", loss_weights=None,
-                 use_noise=False, noise_sd=0.01, lr=0.00001, use_dropout=False, dropout_alpha=0.1, batch_size=128,
-                 epochs=2, kernel_reg=None, l1_weight=0.01, l2_weight=0.01, sgd_momentum=0.9, adam_beta_1=0.9,
-                 adam_beta_2=0.999, epsilon=1e-7, verbose=1, training_var=None, n_output_tasks=1, **kwargs):
+    def __init__(self, hidden_layers=2, hidden_neurons=64, evidential=False, activation="relu", optimizer="adam",
+                 loss_weights=None, use_noise=False, noise_sd=0.01, lr=0.00001, use_dropout=False, dropout_alpha=0.1,
+                 batch_size=128, loss="mse", epochs=2, kernel_reg=None, l1_weight=0.01, l2_weight=0.01,
+                 sgd_momentum=0.9, adam_beta_1=0.9, adam_beta_2=0.999, epsilon=1e-7, verbose=1, training_var=None,
+                 n_inputs=10, n_output_tasks=1, evi_coeff=1.0, uncertainty=False, **kwargs):
 
         super().__init__(**kwargs)
         self.hidden_layers = hidden_layers
         self.hidden_neurons = hidden_neurons
+        self.evidential = evidential
         self.activation = activation
         self.optimizer = optimizer
         self.optimizer_obj = None
@@ -210,6 +347,7 @@ def __init__(self, hidden_layers=2, hidden_neurons=64, activation="relu", optimi
         self.adam_beta_1 = adam_beta_1
         self.adam_beta_2 = adam_beta_2
         self.loss_weights = loss_weights
+        self.loss = loss
         self.lr = lr
         self.kernel_reg = kernel_reg
         self.l1_weight = l1_weight
@@ -225,15 +363,17 @@ def __init__(self, hidden_layers=2, hidden_neurons=64, activation="relu", optimi
         self.training_var = training_var
         self.epsilon = epsilon
         self.n_output_tasks = n_output_tasks
-        self.N_OUTPUT_PARAMS = 4
-        self.hyperparameters = ["hidden_layers", "hidden_neurons", "activation", "training_var",
+        self.n_inputs = n_inputs
+        self.evi_coeff = evi_coeff
+        self.uncertainty = uncertainty
+        self.N_EVI_OUTPUT_PARAMS = 4
+        self.N_DENSE_NORMAL_OUTPUT_PARAMS = 2
+        self.hyperparameters = ["hidden_layers", "evidential", "hidden_neurons", "activation", "training_var",
                                 "optimizer", "sgd_momentum", "adam_beta_1", "adam_beta_2", "epsilon",
-                                "loss_weights", "lr", "kernel_reg", "l1_weight", "l2_weight",
+                                "loss_weights", "lr", "kernel_reg", "l1_weight", "l2_weight", "loss",
                                 "batch_size", "use_noise", "noise_sd", "use_dropout", "dropout_alpha", "epochs",
-                                "verbose", "n_output_tasks", "epsilon"]
+                                "verbose", "n_inputs", "n_output_tasks", "epsilon", "evi_coeff", "uncertainty"]
 
-        # if self.activation == "leaky":
-        #     self.activation = LeakyReLU()
         if self.kernel_reg == "l1":
             self.kernel_reg = L1(self.l1_weight)
         elif self.kernel_reg == "l2":
@@ -242,7 +382,20 @@ def __init__(self, hidden_layers=2, hidden_neurons=64, activation="relu", optimi
             self.kernel_reg = L1L2(self.l1_weight, self.l2_weight)
         else:
             self.kernel_reg = None
+
+        if self.optimizer == "adam":
+            self.optimizer_obj = Adam(learning_rate=self.lr,
+                                      beta_1=self.adam_beta_1,
+                                      beta_2=self.adam_beta_2,
+                                      epsilon=self.epsilon)
+        elif self.optimizer == "sgd":
+            self.optimizer_obj = SGD(learning_rate=self.lr, momentum=self.sgd_momentum)
+
         self.model_layers = []
+        self.model_layers.append(Dense(self.n_inputs,
+                                       activation=self.activation,
+                                       kernel_regularizer=self.kernel_reg,
+                                       name="input_dense"))
         for h in range(self.hidden_layers):
             self.model_layers.append(Dense(self.hidden_neurons,
                                            activation=self.activation,
@@ -253,12 +406,15 @@ def __init__(self, hidden_layers=2, hidden_neurons=64, activation="relu", optimi
             if self.use_noise:
                 self.model_layers.append(GaussianNoise(self.noise_sd, name=f"noise_{h:02d}"))
 
-        self.model_layers.append(DenseNormalGamma(self.n_output_tasks, name="dense_output"))
-
+        if self.evidential:
+            self.model_layers.append(DenseNormalGamma(self.n_output_tasks, name="dense_output"))
+        elif self.uncertainty:
+            self.model_layers.append(DenseNormal(self.n_output_tasks, name="dense_output"))
+        else:
+            self.model_layers.append(Dense(self.n_output_tasks, name="dense_output"))
     def call(self, inputs):
 
         layer_output = self.model_layers[0](inputs)
-
         for l in range(1, len(self.model_layers)):
             layer_output = self.model_layers[l](layer_output)
 
@@ -266,8 +422,13 @@ def call(self, inputs):
 
     def fit(self, x=None, y=None, **kwargs):
 
-        hist = super().fit(x, y, **kwargs)
-        self.training_var = np.var(x, axis=-1)
+        if self.evidential:
+            self.loss = evidential_reg_loss(evi_coef=self.evi_coeff)
+        elif self.uncertainty:
+            self.loss = gaussian_nll
+        super().compile(optimizer=self.optimizer_obj, loss=self.loss)
+        hist = super().fit(x, y, epochs=self.epochs, batch_size=self.batch_size, **kwargs)
+        self.training_var = np.var(y, axis=-1)
 
         return hist
 
@@ -282,16 +443,22 @@ def predict(self, x, return_uncertainties=True, batch_size=1000, **kwargs):
             If return_uncertainties is True: np.array(mu, aleatoric uncertainty, epistemic uncertainty)
             Else If return_uncertainties is False: np.array(mu, gamma, alpha, beta)
         """
-        output = super().predict(x, batch_size=batch_size)
-        if return_uncertainties:
-            return self.calc_uncertainties(output)
+        if type(return_uncertainties) != bool:
+            raise ValueError("return_uncertainties must be a boolean")
+
+        if (not self.evidential) and (not self.uncertainty) and return_uncertainties:
+            raise NotImplementedError("You can only return uncertainty estimates when 'evidential' or 'uncertainty' is"
+                                      " True. Otherwise you can set 'return_uncertainties' to False to return predictions.")
+
+        elif (self.evidential) and return_uncertainties:
+            return self.calc_uncertainties(super().predict(x, batch_size=batch_size))
+
         else:
-            return output
+            return super().predict(x, batch_size=batch_size)
 
     def calc_uncertainties(self, preds):
 
-        mu, v, alpha, beta = np.split(preds, self.N_OUTPUT_PARAMS, axis=-1)
-
+        mu, v, alpha, beta = np.split(preds, self.N_EVI_OUTPUT_PARAMS, axis=-1)
         aleatoric = beta / (alpha - 1)
         epistemic = beta / (v * (alpha - 1))
 
@@ -310,6 +477,4 @@ def get_config(self):
 
         base_config = super().get_config()
         parameter_config = {hp: getattr(self, hp) for hp in self.hyperparameters}
-        return {**base_config, **parameter_config}
-
-
+        return {**base_config, **parameter_config}
\ No newline at end of file
diff --git a/mlguess/tests/test_models.py b/mlguess/tests/test_models.py
index 68293eb..9d481be 100644
--- a/mlguess/tests/test_models.py
+++ b/mlguess/tests/test_models.py
@@ -1,39 +1,24 @@
 import warnings
-import keras
 warnings.filterwarnings("ignore")
-import yaml
 import unittest
 import numpy as np
-from mlguess.keras.models import CategoricalDNN, EvidentialRegressorDNN
-from mlguess.keras.losses import evidential_cat_loss, evidential_reg_loss
+from mlguess.keras.models import CategoricalDNN, RegressorDNN
 from keras.models import load_model
-from mlguess.keras.callbacks import ReportEpoch
 
 class TestModels(unittest.TestCase):
-    def setUp(self):
-        # Load configurations for the models
-        self.mlp_config = "config/surface_layer/mlp.yml"
-        self.gaussian_config = "config/surface_layer/gaussian.yml"
-        self.evidential_config = "config/surface_layer/evidential.yml"
-
-        with open(self.mlp_config) as cf:
-            self.mlp_conf = yaml.load(cf, Loader=yaml.FullLoader)
-
-        with open(self.gaussian_config) as cf:
-            self.gaussian_conf = yaml.load(cf, Loader=yaml.FullLoader)
-
-        with open(self.evidential_config) as cf:
-            self.evidential_conf = yaml.load(cf, Loader=yaml.FullLoader)
 
     def test_evidential_categorical_model(self):
         n_classes = 5
-        epoch_var = keras.Variable(0)
         x_train = np.random.random(size=(10000, 10)).astype('float32')
         y_train = np.random.randint(size=(10000, n_classes), high=4, low=0).astype('float32')
-        report_epoch_callback = ReportEpoch(epoch_var)
-        model = CategoricalDNN(n_classes=n_classes)
-        model.compile(loss=evidential_cat_loss(evi_coef=1.0, epoch_callback=report_epoch_callback), optimizer="adam")
-        hist = model.fit(x_train, y_train, epochs=10, callbacks=report_epoch_callback)
+        model = CategoricalDNN(evidential=True,
+                               activation="leaky_relu",
+                               n_classes=n_classes,
+                               n_inputs=10,
+                               epochs=10,
+                               annealing_coeff=1.5,
+                               lr=0.0001)
+        hist = model.fit(x_train, y_train)
         p_with_uncertainty = model.predict(x_train, return_uncertainties=True, batch_size=10000)
         p_without_uncertainty = model.predict(x_train, return_uncertainties=False, batch_size=10000)
         assert len(p_with_uncertainty) == 4
@@ -45,19 +30,76 @@ def test_evidential_categorical_model(self):
         model.save("test_evi_categorical.keras")
         load_model("test_evi_categorical.keras")
 
+    def test_categorical_model(self):
+        n_classes = 5
+        x_train = np.random.random(size=(10000, 10)).astype('float32')
+        y_train = np.random.randint(size=(10000, n_classes), high=4, low=0).astype('float32')
+        model = CategoricalDNN(evidential=False,
+                               loss="categorical_crossentropy",
+                               output_activation="softmax",
+                               n_classes=n_classes,
+                               n_inputs=10,
+                               epochs=10,
+                               lr=0.0001)
+        hist = model.fit(x_train, y_train)
+        print(model.get_config())
+        print(model.summary())
+        p = model.predict(x_train, return_uncertainties=False, batch_size=10000)
+        assert p.shape[-1] == n_classes
+        assert p.shape[0] == x_train.shape[0]
+        model.save("test_categorical.keras")
+        load_model("test_categorical.keras")
+
     def test_evidential_regression_model(self):
 
-        x_train = np.random.random(size=(10000, 10)).astype('float32')
-        y_train = np.random.random(size=(10000, 1)).astype('float32')
-        model = EvidentialRegressorDNN(hidden_layers=2)
-        model.compile(loss=evidential_reg_loss(0.01), optimizer="adam")
-        model.fit(x_train, y_train, epochs=10)
-        p_with_uncertainty = model.predict(x_train, return_uncertainties=True)
-        p_without_uncertainty = model.predict(x_train, return_uncertainties=False)
-        assert p_with_uncertainty.shape[-1] == 3
-        assert p_without_uncertainty.shape[-1] == 4
-        model.save("test_evi_regression.keras")
-        load_model("test_evi_regression.keras")
+        for n_outputs in range(1, 3):
+            x_train = np.random.random(size=(10000, 10)).astype('float32')
+            y_train = np.random.random(size=(10000, n_outputs)).astype('float32')
+            model = RegressorDNN(hidden_layers=2,
+                                 n_output_tasks=n_outputs,
+                                 evidential=True,
+                                 epochs=10)
+            model.fit(x_train, y_train)
+            p_with_uncertainty = model.predict(x_train, return_uncertainties=True)
+            p_without_uncertainty = model.predict(x_train, return_uncertainties=False)
+            assert p_with_uncertainty.shape[-1] == 3 * n_outputs
+            assert p_without_uncertainty.shape[-1] == 4 * n_outputs
+            model.save("test_evi_regression.keras")
+            load_model("test_evi_regression.keras")
+
+    def test_Gaussian_uncertainty_model(self):
+
+        for n_outputs in range(1, 3):
+            x_train = np.random.random(size=(10000, 10)).astype('float32')
+            y_train = np.random.random(size=(10000, n_outputs)).astype('float32')
+            model = RegressorDNN(hidden_layers=2,
+                                 n_output_tasks=n_outputs,
+                                 evidential=False,
+                                 uncertainty=True,
+                                 epochs=10)
+            model.fit(x_train, y_train)
+            p_with_uncertainty = model.predict(x_train, return_uncertainties=True)
+            p_without_uncertainty = model.predict(x_train, return_uncertainties=False)
+            assert p_with_uncertainty.shape[-1] == 2 * n_outputs
+            assert p_without_uncertainty.shape[-1] == 2 * n_outputs
+            model.save("test_evi_regression.keras")
+            load_model("test_evi_regression.keras")
+
+    def test_standard_nn_regression(self):
+
+        for n_outputs in range(1, 3):
+            x_train = np.random.random(size=(10000, 10)).astype('float32')
+            y_train = np.random.random(size=(10000, n_outputs)).astype('float32')
+            model = RegressorDNN(hidden_layers=2,
+                                 n_output_tasks=n_outputs,
+                                 evidential=False,
+                                 uncertainty=False,
+                                 epochs=10)
+            model.fit(x_train, y_train)
+            p_without_uncertainty = model.predict(x_train, return_uncertainties=False)
+            assert p_without_uncertainty.shape[-1] == n_outputs
+            model.save("test_evi_regression.keras")
+            load_model("test_evi_regression.keras")
 
 if __name__ == "__main__":
     unittest.main()
\ No newline at end of file
diff --git a/notebooks/evidential_figure_1.ipynb b/notebooks/evidential_figure_1.ipynb
new file mode 100644
index 0000000..40c5185
--- /dev/null
+++ b/notebooks/evidential_figure_1.ipynb
@@ -0,0 +1,528 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "cd812892-54a8-4e15-a279-30136393b534",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import xarray as xr \n",
+    "import pandas as pd\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "from matplotlib.colors import LogNorm\n",
+    "from scipy.stats import dirichlet\n",
+    "from mlguess.keras.models import CategoricalDNN\n",
+    "import yaml\n",
+    "from bridgescaler import load_scaler\n",
+    "from keras.models import load_model\n",
+    "from matplotlib.patches import Polygon\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "076feff0-1310-4b49-8c01-6dbc20c6eb75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fn_config = \"/glade/p/cisl/aiml/ai2es/winter_ptypes/models/evidential_unweighted/model.yml\"\n",
+    "with open(fn_config) as cf:\n",
+    "    conf = yaml.load(cf, Loader=yaml.FullLoader)\n",
+    "input_features = (\n",
+    "    conf[\"TEMP_C\"] + conf[\"T_DEWPOINT_C\"] + conf[\"UGRD_m/s\"] + conf[\"VGRD_m/s\"]\n",
+    ")\n",
+    "output_features = conf[\"ptypes\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "d5a38601-6962-4cfe-8dee-0979c78807fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/work/dgagne/conda-envs/guess/lib/python3.10/site-packages/keras/src/layers/activations/leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n",
+      "  warnings.warn(\n",
+      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
+      "WARNING:absl:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.layers import LeakyReLU\n",
+    "model = load_model(\"/glade/campaign/cisl/aiml/ai2es/winter_ptypes/models/evidential_unweighted/models/model_19.h5\", custom_objects={\"LeakyReLU\":LeakyReLU})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "a98e928b-b2a3-4702-8d52-6ed963f10472",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_data = pd.read_parquet(\"/glade/p/cisl/aiml/ai2es/winter_ptypes/models/evidential_unweighted/evaluate/test_19.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "26bcf505-b5b3-4538-8ef4-0854bc3bc9da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_scaler_loc = os.path.join(conf[\"save_loc\"], \"scalers\", \"input.json\")\n",
+    "\n",
+    "input_scaler = load_scaler(input_scaler_loc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "f69efb1b-6ab7-48c6-be99-38071a714514",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaled_input_x = input_scaler.transform(test_data[input_features])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "73858855-3c04-4c2b-82d9-1ecead01aff0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_preds = model.predict(scaled_input_x, batch_size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "c2ad51a1-e761-4292-a68d-89238ad09533",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "317229"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_preds[:, 2].argmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "4030db6d-8083-4ae7-b7d0-90f37b507f79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([317229, 317359, 317524, 317671, 317529, 317537, 317360, 317375,\n",
+       "       317856])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_preds[:, 2].argsort()[-1:-10:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "e1f71ddc-de01-4ad0-858d-c194d41934cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps = np.arange(0, 1.01, 0.01)\n",
+    "all_ps = np.meshgrid(ps, 1-ps)\n",
+    "p_db = np.vstack([all_p.ravel() for all_p in all_ps]).T\n",
+    "p_simp = p_db[p_db.sum(axis=1)==1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "742b56ef-9bd6-4329-884f-df3488fb34ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heights = np.arange(0, 5250.0, 250)\n",
+    "temps = np.linspace(0, -20, heights.size)\n",
+    "dewps = np.linspace(-10, -30, heights.size)\n",
+    "us = np.linspace(-10, 30, heights.size)\n",
+    "vs = np.linspace(-5, 10, heights.size)\n",
+    "px = np.arange(0, 1, 0.01)\n",
+    "pall = np.concatenate([px, 1-px]).reshape(2, px.size)\n",
+    "dp = dirichlet.pdf(pall, [10, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "55529174-8724-4963-9b71-3be2c577e5ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = 317529\n",
+    "temps = test_data.iloc[idx][conf[\"TEMP_C\"]]\n",
+    "dewps = test_data.iloc[idx][conf[\"T_DEWPOINT_C\"]]\n",
+    "us = test_data.iloc[idx][conf[\"UGRD_m/s\"]]\n",
+    "vs = test_data.iloc[idx][conf[\"VGRD_m/s\"]]\n",
+    "heights = np.arange(0, 5250.0, 250)\n",
+    "test_ev = np.maximum(test_preds[idx], 0)\n",
+    "test_alpha = test_ev + 1\n",
+    "dp = dirichlet.pdf(p_simp.T, test_alpha[2:])\n",
+    "dp_ens = dirichlet.rvs(test_alpha[2:], size=20)\n",
+    "test_belief = np.concatenate([test_ev / test_alpha.sum(), [test_ev.shape[0] / test_alpha.sum()]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "341040f2-2eca-4c32-b6aa-68d06eacbfbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = test_alpha.sum()\n",
+    "evi_epi = (test_alpha[2] / s * (1 - test_alpha[2] / s)) / (s + 1)\n",
+    "evi_ale = test_alpha[2] / s - (test_alpha[2] / s) ** 2 - evi_epi\n",
+    "ens_epi = np.var(dp_ens[:, 0])\n",
+    "ens_ale = np.mean(dp_ens[:, 0] * (1 - dp_ens[:, 0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "c4775ad2-efbc-4fb4-9316-a9805158d505",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.       ,  1.       , 14.175278 ,  2.8372083], dtype=float32)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_alpha"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "2845aa14-2bfa-4f29-a6fe-39c09d275d0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.14423118590624248\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_evi_epi = 0\n",
+    "s = test_alpha.sum()\n",
+    "for i in range(4):\n",
+    "    total_evi_epi += (test_alpha[i] / s * (1 - test_alpha[i] / s)) / (s + 1)\n",
+    "print(np.sqrt(total_evi_epi))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "bc616e67-0b71-4b30-af25-0d5953849be3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0973584756519364 0.4245151691664927\n",
+      "0.07338351822859498 0.36650745085779707\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.sqrt(evi_epi), np.sqrt(evi_ale))\n",
+    "print(np.sqrt(ens_epi), np.sqrt(ens_ale))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "f0702c55-2228-4ae5-bc2a-db5794943e65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.009478672781268693"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evi_epi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "6eee3b89-646f-4c74-ad6c-0a7e8e6f292e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(tight_layout=True, figsize=(7, 6))\n",
+    "gs = gridspec.GridSpec(4, 2, height_ratios=[0.5, 0.2, 0.3, 0.1])\n",
+    "ax = fig.add_subplot(gs[0,0])\n",
+    "ax.plot(temps, heights / 1000, color='r', label=\"Temperature\")\n",
+    "ax.plot(dewps, heights / 1000, color='g', label=\"Dewpoint\", linestyle='--')\n",
+    "ax.set_xticks(np.arange(-20, 10, 5))\n",
+    "ax.set_xlim(-20, 5)\n",
+    "ax.legend(fontsize=8)\n",
+    "ax.set_xlabel(\"Temperature ($\\degree$C)\")\n",
+    "ax.set_ylabel(\"Height (km)\")\n",
+    "ax.text(-22, 4.8, \"(a)\",\n",
+    "      fontsize=12, va='top', ha='right')\n",
+    "ax.plot(np.zeros(heights.shape[0]), heights / 1000, 'k--', lw=0.5)\n",
+    "ax.set_ylim(0, 5)\n",
+    "\n",
+    "ax = fig.add_subplot(gs[0, 1])\n",
+    "cmap = plt.get_cmap(\"Set1\", 5)\n",
+    "levels = [\"0-1 km\", \"1-2 km\", \"2-3 km\", \"3-4 km\", \"4-5 km\"]\n",
+    "hodo_ticks = np.arange(-10, 40, 10)\n",
+    "for i in range(5):\n",
+    "    ax.plot(us[4 * i: 4 * i +5], vs[4* i:4 * i + 5], marker='o', color=cmap.colors[i], label=levels[i])\n",
+    "ax.plot(np.zeros(hodo_ticks.shape), hodo_ticks, 'k--', lw=0.5)\n",
+    "ax.plot(hodo_ticks, np.zeros(hodo_ticks.shape),  'k--', lw=0.5)\n",
+    "\n",
+    "ax.set_xlabel(\"Zonal Wind (m s$^{-1}$)\")\n",
+    "ax.set_ylabel(\"Meridional Wind (m s$^{-1}$)\")\n",
+    "ax.set_xticks(hodo_ticks)\n",
+    "ax.set_yticks(hodo_ticks)\n",
+    "ax.legend(fontsize=8)\n",
+    "ax.text(-25, 31, \"(b)\",\n",
+    "      fontsize=12, va='top', ha='right')\n",
+    "\n",
+    "ax = fig.add_subplot(gs[1, :])\n",
+    "poly = Polygon([[-2.5,0],[2.5, 0], [5, 5], [-5, 5]], color=\"purple\", clip_on=False)\n",
+    "ax.add_patch(poly)\n",
+    "ax.set_xlim(-5, 5)\n",
+    "ax.set_ylim(2, 5)\n",
+    "ax.text(0, 2.5, \"Evidential or Ensemble Neural Networks\", fontsize=12, color='w', ha=\"center\", va=\"center\")\n",
+    "ax.set_axis_off()\n",
+    "#s1 = Polygon([[-1, -1], [-0.5, -1], [-0.5, -0.5], [-1, -0.5]], clip_on=False)\n",
+    "#ax.add_patch(s1)\n",
+    "ax = fig.add_subplot(gs[2, :])\n",
+    "#ax.bar([0, 1], test_ev[2:][::-1], 0.02)\n",
+    "ax.plot(p_simp[:, 0],dp, color='tomato', label=\"Evidential PDF\")\n",
+    "ax.hist(dp_ens[:, 0], np.arange(0, 1.05, .05), density=True, color='skyblue', label=\"Ensemble PDF\")\n",
+    "ax.bar([test_ev[2]/test_ev.sum()], [dp.max()], 0.01, color='r', label=\"Evidential Expected Value\")\n",
+    "ax.bar([dp_ens[:, 0].mean()], [dp.max()], 0.01, color='b', label=\"Ensemble Mean\")\n",
+    "ax.legend(fontsize=8)\n",
+    "ax.set_xticks(np.arange(0, 1.1, 0.1))\n",
+    "ax.set_xlim(0, 1)\n",
+    "ax.set_xlabel(\"Probability of Sleet\")\n",
+    "ax.set_ylabel(\"Density\")\n",
+    "ax.text(-.04, 7, \"(c)\", fontsize=12, va='top', ha='center')\n",
+    "\n",
+    "ax = fig.add_subplot(gs[3, :])\n",
+    "#ax.bar([0, 1], test_ev[2:][::-1], 0.02)\n",
+    "p_type_colors = [\"green\", \"blue\", \"orange\", \"red\", \"purple\"]\n",
+    "p_type_labels = [\"Rain\", \"Snow\", \"Sleet\", \"Freezing\\nRain\", \"Unknown\"]\n",
+    "for b in range(test_belief.shape[0]):\n",
+    "    if b == 0:\n",
+    "        ax.barh(0.5, test_belief[b], height=1, left=0, color=p_type_colors[b])\n",
+    "    else:\n",
+    "        ax.barh(0.5, test_belief[b], height=1, left=test_belief.cumsum()[b-1], color=p_type_colors[b])\n",
+    "    if test_belief[b] > 0:\n",
+    "        if b == 0:\n",
+    "            ax.text((0 + test_belief.cumsum()[b]) / 2, 0.5, p_type_labels[b], \n",
+    "                    ha=\"center\", va=\"center\", fontsize=8, color='white')\n",
+    "        else:\n",
+    "            ax.text((test_belief.cumsum()[b-1] + test_belief.cumsum()[b]) / 2, 0.5, p_type_labels[b], \n",
+    "                    ha=\"center\", va=\"center\", fontsize=8, color='white')\n",
+    "    ax.set_ylim(0, 1)\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_yticks([])\n",
+    "    ax.set_xticks(np.arange(0, 1.1, 0.1))\n",
+    "ax.set_xlabel(\"Precipitation Type Belief Mass\")\n",
+    "ax.text(-0.04, 0.8, \"(d)\", fontsize=12, va='top', ha='center')\n",
+    "\n",
+    "plt.savefig(\"evidential_figure_1.png\", dpi=300, bbox_inches=\"tight\")\n",
+    "plt.savefig(\"evidential_figure_1.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "id": "e03d241c-0cd1-4144-abad-8b281606210b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = 317529\n",
+    "temps = test_data.iloc[idx][conf[\"TEMP_C\"]]\n",
+    "dewps = test_data.iloc[idx][conf[\"T_DEWPOINT_C\"]]\n",
+    "us = test_data.iloc[idx][conf[\"UGRD_m/s\"]]\n",
+    "vs = test_data.iloc[idx][conf[\"VGRD_m/s\"]]\n",
+    "heights = np.arange(0, 5250.0, 250)\n",
+    "test_ev = np.maximum(test_preds[idx], 0)\n",
+    "test_alpha = test_ev + 1\n",
+    "dst_u = test_ev.shape[0] / test_alpha.sum()\n",
+    "dp = dirichlet.pdf(p_simp.T, test_alpha[2:])\n",
+    "dp_ens = dirichlet.rvs(test_alpha[2:], size=20) * (1-dst_u)\n",
+    "test_belief = np.concatenate([test_ev / test_alpha.sum(), [dst_u]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "id": "9df43f96-f0eb-43d9-8697-6d30f4be56f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(tight_layout=True)\n",
+    "gs = gridspec.GridSpec(3, 2, height_ratios=[0.6, 0.3, 0.1])\n",
+    "ax = fig.add_subplot(gs[0,0])\n",
+    "ax.plot(temps, heights / 1000, color='r', label=\"Temperature\")\n",
+    "ax.plot(dewps, heights / 1000, color='g', label=\"Dewpoint\", linestyle='--')\n",
+    "ax.set_xticks(np.arange(-20, 10, 5))\n",
+    "ax.set_xlim(-20, 5)\n",
+    "ax.legend(fontsize=8)\n",
+    "ax.set_xlabel(\"Temperature (C)\")\n",
+    "ax.set_ylabel(\"Height (km)\")\n",
+    "ax.plot(np.zeros(heights.shape[0]), heights / 1000, 'k--', lw=0.5)\n",
+    "ax.set_ylim(0, 5)\n",
+    "ax = fig.add_subplot(gs[0, 1])\n",
+    "cmap = plt.get_cmap(\"Set1\", 5)\n",
+    "levels = [\"0-1 km\", \"1-2 km\", \"2-3 km\", \"3-4 km\", \"4-5 km\"]\n",
+    "hodo_ticks = np.arange(-10, 40, 10)\n",
+    "for i in range(5):\n",
+    "    ax.plot(us[4 * i: 4 * i +5], vs[4* i:4 * i + 5], marker='o', color=cmap.colors[i], label=levels[i])\n",
+    "ax.plot(np.zeros(hodo_ticks.shape), hodo_ticks, 'k--', lw=0.5)\n",
+    "ax.plot(hodo_ticks, np.zeros(hodo_ticks.shape),  'k--', lw=0.5)\n",
+    "\n",
+    "ax.set_xlabel(\"West-East Wind (m/s)\")\n",
+    "ax.set_ylabel(\"South-North Wind (m/s)\")\n",
+    "ax.set_xticks(hodo_ticks)\n",
+    "ax.set_yticks(hodo_ticks)\n",
+    "ax.legend(fontsize=8)\n",
+    "\n",
+    "ax = fig.add_subplot(gs[1, :])\n",
+    "#ax.bar([0, 1], test_ev[2:][::-1], 0.02)\n",
+    "ax.plot(p_simp[:, 0] * (1-dst_u),dp, color='tomato', label=\"Evidential PDF\")\n",
+    "ax.hist(dp_ens[:, 0], np.arange(0, 1.05, .05), density=True, color='skyblue', label=\"Ensemble PDF\")\n",
+    "ax.bar([test_ev[2]/test_ev.sum() * (1-dst_u)], [dp.max()], 0.01, color='r', label=\"Evidential Expected Value\")\n",
+    "ax.bar([dp_ens[:, 0].mean()], [dp.max()], 0.01, color='b', label=\"Ensemble Mean\")\n",
+    "ax.legend(fontsize=8)\n",
+    "ax.set_xticks(np.arange(0, 1.1, 0.1))\n",
+    "ax.set_xlim(0, 1)\n",
+    "ax.set_xlabel(\"Probability of Sleet\")\n",
+    "ax.set_ylabel(\"Density\")\n",
+    "\n",
+    "ax = fig.add_subplot(gs[2, :])\n",
+    "#ax.bar([0, 1], test_ev[2:][::-1], 0.02)\n",
+    "p_type_colors = [\"green\", \"blue\", \"orange\", \"red\", \"purple\"]\n",
+    "p_type_labels = [\"Rain\", \"Snow\", \"Sleet\", \"Freezing\\nRain\", \"Unknown\"]\n",
+    "for b in range(test_belief.shape[0]):\n",
+    "    if b == 0:\n",
+    "        ax.barh(0.5, test_belief[b], height=1, left=0, color=p_type_colors[b])\n",
+    "    else:\n",
+    "        ax.barh(0.5, test_belief[b], height=1, left=test_belief.cumsum()[b-1], color=p_type_colors[b])\n",
+    "    if test_belief[b] > 0:\n",
+    "        if b == 0:\n",
+    "            ax.text((0 + test_belief.cumsum()[b]) / 2, 0.5, p_type_labels[b], \n",
+    "                    ha=\"center\", va=\"center\", fontsize=8, color='white')\n",
+    "        else:\n",
+    "            ax.text((test_belief.cumsum()[b-1] + test_belief.cumsum()[b]) / 2, 0.5, p_type_labels[b], \n",
+    "                    ha=\"center\", va=\"center\", fontsize=8, color='white')\n",
+    "    ax.set_ylim(0, 1)\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_yticks([])\n",
+    "    ax.set_xticks(np.arange(0, 1.1, 0.1))\n",
+    "ax.set_xlabel(\"Precipitation Type Belief Mass\")\n",
+    "plt.savefig(\"evidential_figure_1_bel.png\", dpi=300, bbox_inches=\"tight\")\n",
+    "plt.savefig(\"evidential_figure_1_bel.pdf\", bbox_inches=\"tight\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:guess]",
+   "language": "python",
+   "name": "conda-env-guess-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/paper_figures_categorical.ipynb b/notebooks/paper_figures_categorical.ipynb
index 71658bc..9b84041 100644
--- a/notebooks/paper_figures_categorical.ipynb
+++ b/notebooks/paper_figures_categorical.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 1,
    "id": "8cfcfa19-2637-4c90-bb0e-88144c89326a",
    "metadata": {},
    "outputs": [],
@@ -25,31 +25,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "0d87b662-c52e-4ecc-bcc8-5c119cd1c9ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df = pd.read_parquet(\"/glade/scratch/schreck/repos/evidential/results/ptype/weighted/evidential/evaluate/test_7.parquet\")\n",
-    "#df = pd.read_parquet(\"/glade/scratch/schreck/repos/evidential/results/ptype/weighted/production/evidential/evaluate/test_7.parquet\")\n",
-    "df = pd.read_parquet(\"/glade/campaign/cisl/aiml/ai2es/winter_ptypes/models/evidential_unweighted/evaluate/test_0.parquet\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "616d4d32-2191-49b5-9be9-ed40adc2da87",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#df_det = pd.read_parquet(\"/glade/scratch/schreck/repos/evidential/results/ptype/weighted/production/classifier/evaluate/test_7.parquet\")\n",
     "df = pd.read_parquet(\"/glade/campaign/cisl/aiml/ai2es/winter_ptypes/models/evidential_unweighted/evaluate/test_0.parquet\")\n",
     "df_det = pd.read_parquet(\"/glade/campaign/cisl/aiml/ai2es/winter_ptypes/models/classifier_unweighted/evaluate/test_0.parquet\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 38,
    "id": "4aca5f16-ef17-41ee-b077-f6c1a5cd5024",
    "metadata": {},
    "outputs": [],
@@ -77,52 +64,21 @@
     "df[\"epi_sum\"] = epi_sum\n",
     "\n",
     "df[\"total\"] = total\n",
-    "df[\"total_sum\"] = df[\"epi_sum\"] + df[\"ale_sum\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "9a8268ab-cb7c-4bee-abae-353cdb5164db",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x1459d45fbc10>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist2d(np.sqrt(df[\"epi_sum\"]), np.sqrt(df[\"ale_sum\"]) , bins=50, cmap=\"viridis_r\", cmin=1, norm=LogNorm())\n",
-    "plt.colorbar()"
+    "df[\"total_sum\"] = df[\"epi_sum\"] + df[\"ale_sum\"]\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "id": "2dd6cfd3-545e-490b-a7af-f89cdceeaf51",
+   "execution_count": 39,
+   "id": "9a1f5ba7-bf66-4b73-848b-6dd31fbe0711",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 10/10 [01:07<00:00,  6.74s/it]\n"
+      "100%|██████████| 10/10 [00:50<00:00,  5.02s/it]\n"
      ]
     }
    ],
@@ -146,38 +102,7 @@
     "    ensemble_entropy[split] = dfe[\"entropy\"]\n",
     "    ensemble_mutual[split] = dfe[\"mutual_info\"]\n",
     "    ensemble_probs[split] = dfe[[f\"pred_conf{k+1:d}\" for k in range(4)]].values\n",
-    "    #ensemble_ale[split] = dfe[[f\"aleatoric{k+1}\" for k in range(4)]].sum(axis=1)\n",
-    "    #ensemble_epi[split] = dfe[[f\"epistemic{k+1}\" for k in range(4)]].sum(axis=1)\n",
-    "\n",
-    "ensemble_mean = ensemble_probs.mean(axis=0)\n",
-    "#ensemble_epi = ensemble_probs - ensemble_mean\n",
-    "\n",
-    "## Compute averages, uncertainties \n",
-    "#df_det[\"ave_conf\"] = np.mean(ensemble_p, axis = 0)\n",
-    "#df_det[\"ave_entropy\"] = np.mean(ensemble_entropy, axis = 0)\n",
-    "#df_det[\"ave_mutual_info\"] = np.mean(ensemble_mutual, axis = 0)\n",
-    "\n",
-    "#df_det[\"epistemic\"] = df_det[\"epistemic\"] #np.var(ensemble_p, axis = 0)\n",
-    "#df_det[\"aleatoric\"] = df_det[\"aleatoric\"] #np.mean(ensemble_std, axis = 0)\n",
-    "#df_det[\"total\"] = df_det[\"epistemic\"] + df_det[\"aleatoric\"]\n",
     "\n",
-    "# df_det[\"ale_sum\"] = np.sqrt(np.mean(ensemble_ale, axis = 0))\n",
-    "# df_det[\"epi_sum\"] = np.sqrt(np.mean(ensemble_epi, axis = 0))\n",
-    "#ale_sum = df_det[[f\"aleatoric{k+1}\" for k in range(4)]].sum(axis=1)\n",
-    "#epi_sum = df_det[[f\"epistemic{k+1}\" for k in range(4)]].sum(axis=1)\n",
-    "\n",
-    "#df_det[\"ale_sum\"] = ale_sum\n",
-    "#df_det[\"epi_sum\"] = epi_sum\n",
-    "#df_det[\"total_sum\"] = ale_sum + epi_sum"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "df8e1a54-f083-4ef9-af0a-8f6c993d63ff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "ens_total_epi = np.sum(np.mean((ensemble_probs - ensemble_mean) ** 2, axis=0), axis=1)\n",
     "ens_total_ale = np.sum(np.mean((ensemble_probs * (1-ensemble_probs)) ** 2, axis=0), axis=1)\n",
     "df_ens_stats = pd.DataFrame(dict(ale_sum=ens_total_ale, epi_sum=ens_total_epi, \n",
@@ -190,415 +115,133 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "b7b64bc2-716f-4c27-a3c2-193e4237dd05",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([1.4000e+01, 2.7700e+02, 1.1880e+03, 3.0100e+03, 5.5740e+03,\n",
-       "        8.0310e+03, 9.8130e+03, 1.1213e+04, 1.1983e+04, 1.1891e+04,\n",
-       "        1.1769e+04, 1.1123e+04, 1.0458e+04, 9.3770e+03, 8.4430e+03,\n",
-       "        8.1540e+03, 7.8330e+03, 7.9290e+03, 3.5860e+03, 9.4880e+03,\n",
-       "        6.8410e+03, 6.7860e+03, 6.2660e+03, 5.6220e+03, 5.4440e+03,\n",
-       "        7.5770e+03, 1.7411e+04, 4.7188e+04, 9.1113e+04, 5.1294e+04]),\n",
-       " array([-44.46859605, -43.02464511, -41.58069416, -40.13674322,\n",
-       "        -38.69279228, -37.24884134, -35.8048904 , -34.36093946,\n",
-       "        -32.91698852, -31.47303758, -30.02908664, -28.5851357 ,\n",
-       "        -27.14118476, -25.69723382, -24.25328287, -22.80933193,\n",
-       "        -21.36538099, -19.92143005, -18.47747911, -17.03352817,\n",
-       "        -15.58957723, -14.14562629, -12.70167535, -11.25772441,\n",
-       "         -9.81377347,  -8.36982253,  -6.92587158,  -5.48192064,\n",
-       "         -4.0379697 ,  -2.59401876,  -1.15006782]),\n",
-       " <BarContainer object of 30 artists>)"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(np.log(np.sqrt(ens_total_epi)), bins=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "cc324a39-5da2-4526-8e31-0101333adec5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pred_conf1</th>\n",
-       "      <th>pred_conf2</th>\n",
-       "      <th>pred_conf3</th>\n",
-       "      <th>pred_conf4</th>\n",
-       "      <th>true_label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.983192</td>\n",
-       "      <td>0.005603</td>\n",
-       "      <td>0.005603</td>\n",
-       "      <td>0.005603</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.983852</td>\n",
-       "      <td>0.005383</td>\n",
-       "      <td>0.005383</td>\n",
-       "      <td>0.005383</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.326530</td>\n",
-       "      <td>0.176136</td>\n",
-       "      <td>0.262648</td>\n",
-       "      <td>0.234685</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.066775</td>\n",
-       "      <td>0.617522</td>\n",
-       "      <td>0.181781</td>\n",
-       "      <td>0.133922</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.045006</td>\n",
-       "      <td>0.864982</td>\n",
-       "      <td>0.045006</td>\n",
-       "      <td>0.045006</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>0.989291</td>\n",
-       "      <td>0.003570</td>\n",
-       "      <td>0.003570</td>\n",
-       "      <td>0.003570</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>0.983164</td>\n",
-       "      <td>0.005612</td>\n",
-       "      <td>0.005612</td>\n",
-       "      <td>0.005612</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>0.982188</td>\n",
-       "      <td>0.005937</td>\n",
-       "      <td>0.005937</td>\n",
-       "      <td>0.005937</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>0.984302</td>\n",
-       "      <td>0.005233</td>\n",
-       "      <td>0.005233</td>\n",
-       "      <td>0.005233</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>0.988969</td>\n",
-       "      <td>0.003677</td>\n",
-       "      <td>0.003677</td>\n",
-       "      <td>0.003677</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>396696 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    pred_conf1  pred_conf2  pred_conf3  pred_conf4  true_label\n",
-       "0     0.983192    0.005603    0.005603    0.005603           0\n",
-       "1     0.983852    0.005383    0.005383    0.005383           0\n",
-       "2     0.326530    0.176136    0.262648    0.234685           3\n",
-       "3     0.066775    0.617522    0.181781    0.133922           1\n",
-       "4     0.045006    0.864982    0.045006    0.045006           1\n",
-       "..         ...         ...         ...         ...         ...\n",
-       "21    0.989291    0.003570    0.003570    0.003570           0\n",
-       "22    0.983164    0.005612    0.005612    0.005612           0\n",
-       "23    0.982188    0.005937    0.005937    0.005937           0\n",
-       "24    0.984302    0.005233    0.005233    0.005233           0\n",
-       "26    0.988969    0.003677    0.003677    0.003677           0\n",
-       "\n",
-       "[396696 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[[f\"pred_conf{x:d}\" for x in range(1, 5)] + [\"true_label\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8f2abd0f-27ad-4716-a6e1-58ab90e91ea2",
+   "cell_type": "markdown",
+   "id": "49fec456-404f-4494-aaf8-f8508e49f5c4",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "#df_det = pd.read_parquet(\"/glade/scratch/schreck/repos/evidential/results/ptype/weighted/classifier/evaluate/test_7.parquet\")"
+    "# Reliability Plots (Figure 3)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "7aa45196-74df-4fe8-87f0-a3ea9a42c10d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# n_splits = 10\n",
-    "\n",
-    "# ensemble_p = np.zeros((n_splits, df_det.shape[0]))\n",
-    "# ensemble_std = np.zeros((n_splits, df_det.shape[0]))\n",
-    "# ensemble_entropy = np.zeros((n_splits, df_det.shape[0]))\n",
-    "# ensemble_mutual = np.zeros((n_splits, df_det.shape[0]))\n",
-    "# ensemble_p_sum = np.zeros((n_splits, df_det.shape[0], 4))\n",
-    "# ensemble_std_sum = np.zeros((n_splits, 4, df_det.shape[0]))\n",
-    "\n",
-    "# # Loop over ensemble of parametric models\n",
-    "# for split in tqdm.tqdm(range(n_splits)):\n",
-    "#     dfe = pd.read_parquet(os.path.join(\"/glade/scratch/schreck/repos/evidential/results/ptype/weighted/classifier\", \"evaluate\", f\"test_{split}.parquet\"))\n",
-    "#     ensemble_p[split] = dfe[\"pred_conf\"]\n",
-    "#     ensemble_std[split] = dfe[\"pred_conf\"] * (1 - dfe[\"pred_conf\"])\n",
-    "#     ensemble_entropy[split] = dfe[\"entropy\"]\n",
-    "#     ensemble_mutual[split] = dfe[\"mutual_info\"]\n",
-    "    \n",
-    "# # Compute averages, uncertainties \n",
-    "# df_det[\"ave_conf\"] = np.mean(ensemble_p, axis = 0)\n",
-    "# df_det[\"ave_entropy\"] = np.mean(ensemble_entropy, axis = 0)\n",
-    "# df_det[\"ave_mutual_info\"] = np.mean(ensemble_mutual, axis = 0)\n",
-    "# df_det[\"epistemic\"] = np.var(ensemble_p, axis = 0)\n",
-    "# df_det[\"aleatoric\"] = np.mean(ensemble_std, axis = 0)\n",
-    "# df_det[\"total\"] = df_det[\"epistemic\"] + df_det[\"aleatoric\"]\n",
-    "\n",
-    "# df_det[\"epistemic\"] = np.sqrt(df_det[\"epistemic\"])\n",
-    "# df_det[\"aleatoric\"] = np.sqrt(df_det[\"aleatoric\"])\n",
-    "# df_det[\"total\"] = np.sqrt(df_det[\"total\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "5b383240-7a4e-4922-a7b4-d25adcd63c7e",
+   "id": "b663bfaa-2af2-41c3-afa0-e02dd293cce2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.colors as colors\n",
-    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
-    "\n",
-    "\n",
-    "def plot_uncertainties(\n",
-    "    dfs,\n",
-    "    input_cols,\n",
-    "    output_cols,\n",
-    "    num_bins=20,\n",
-    "    legend_cols=None,\n",
-    "    x_labels=None,\n",
-    "    y_labels=None,\n",
-    "    fontsize=10,\n",
-    "    save_location=None,\n",
-    "    top1=True\n",
-    "):\n",
-    "\n",
-    "    width = 10\n",
-    "    height = 3.5\n",
-    "    fig, axs = plt.subplots(1, len(dfs), figsize=(width, height), sharey = \"col\")#, sharey = \"col\", sharex = \"row\")\n",
-    "\n",
-    "    if legend_cols is None:\n",
-    "        legend_cols = output_cols\n",
-    "        \n",
-    "    for i, df in enumerate(dfs):\n",
-    "\n",
-    "        not_nan = np.isfinite(df[input_cols + output_cols])\n",
-    "        df = df[not_nan].copy()\n",
-    "\n",
-    "        # Loop over each element in output_cols and create a hexbin plot\n",
-    "        for z, (i_col, o_col) in enumerate(zip(input_cols, output_cols)):\n",
-    "            # Calculate the mean prediction for the current column\n",
-    "            x_array = np.sqrt(df[o_col].copy())\n",
-    "            y_array = np.sqrt(df[i_col].copy())\n",
-    "            \n",
-    "            # Create logarithmic bin edges\n",
-    "            x_bins = np.logspace(\n",
-    "                1e-4,\n",
-    "                1e1,\n",
-    "                num=num_bins + 1,\n",
-    "            )\n",
-    "            y_bins = np.logspace(\n",
-    "                1e-4, # np.log10(np.nanmin(y_array))\n",
-    "                1e1, # np.log10(np.nanmax(y_array))\n",
-    "                num=num_bins + 1,\n",
-    "            )\n",
-    "\n",
-    "            # Create the 2D histogram plot\n",
-    "            my_range = [\n",
-    "                [np.nanpercentile(x_array, 0), np.nanpercentile(x_array, 100)],\n",
-    "                [np.nanpercentile(y_array, 0), np.nanpercentile(y_array, 100)],\n",
-    "            ]\n",
-    "            \n",
-    "            # Create the histograms\n",
-    "            hist1, xedges1, yedges1 = np.histogram2d(x_array, y_array, bins=num_bins)\n",
-    "            # Calculate vmin and vmax\n",
-    "            vmin = np.min(hist1)\n",
-    "            vmax = np.max(hist1)\n",
-    "            print(vmin, vmax)\n",
-    "            \n",
-    "            # Create the 2D histogram plot\n",
-    "            hb = axs[i].hist2d(\n",
-    "                x_array,\n",
-    "                y_array,\n",
-    "                bins=num_bins,\n",
-    "                cmap=\"viridis_r\",\n",
-    "                norm=colors.LogNorm(vmax=153027.0),\n",
-    "            )\n",
+    "true_labels = df[\"true_label\"].values\n",
+    "pred_labels = df[\"pred_label\"]\n",
+    "pred_probs = df[[f\"pred_conf{k+1}\" for k in range(4)]].values\n",
+    "confidences = df[\"pred_conf\"]\n",
     "\n",
-    "            # Set the axis labels\n",
-    "            # axs[i].set_title(legend_cols[i], fontsize=fontsize)\n",
-    "            axs[i].set_xlabel(x_labels[z], fontsize=fontsize)\n",
-    "            if i == 0:\n",
-    "                axs[i].set_ylabel(y_labels[z], fontsize=fontsize)\n",
-    "            axs[i].tick_params(axis=\"both\", which=\"major\", labelsize=fontsize)\n",
-    "\n",
-    "            # Move the colorbar below the x-axis label\n",
-    "            cbar = axs[i].figure.colorbar(hb[-1], ax=axs[i], pad = 0.025)#, shrink=0.80)\n",
-    "            cbar.set_label(\"Count\")\n",
-    "\n",
-    "            # Set the tick labels to use scientific notation\n",
-    "            axs[i].ticklabel_format(style=\"sci\", axis=\"both\", scilimits=(-1, 1))\n",
-    "\n",
-    "            # Add 1-1 line\n",
-    "            smaller = min(min(x_array), min(y_array))\n",
-    "            larger = max(max(x_array), max(y_array))\n",
-    "            axs[i].plot(\n",
-    "                np.linspace(smaller, larger, 10),\n",
-    "                np.linspace(smaller, larger, 10),\n",
-    "                ls=\"--\",\n",
-    "                c=\"k\",\n",
-    "            )\n",
-    "            \n",
-    "    axs[0].set_title(\"Ensemble MLP\", fontsize = 10)\n",
-    "    axs[1].set_title(\"Evidential MLP\", fontsize = 10)\n",
-    "\n",
-    "    if top1:\n",
-    "        axs[0].set_xlim([0, 0.275])\n",
-    "        axs[0].set_ylim([0, 0.505])\n",
-    "        axs[1].set_xlim([0, 0.275])\n",
-    "        axs[1].set_ylim([0, 0.505])\n",
-    "        axs[0].text(-0.075, 1.1, \"(a)\", transform=axs[0].transAxes,\n",
-    "          fontsize=10, va='top', ha='right')\n",
-    "\n",
-    "        axs[1].text(-0.05, 1.1, \"(b)\", transform=axs[1].transAxes,\n",
-    "          fontsize=10, va='top', ha='right')\n",
-    "    else:\n",
-    "        axs[0].set_xlim([0, 0.39])\n",
-    "        axs[0].set_ylim([0, 0.87])\n",
-    "        axs[1].set_xlim([0, 0.39])\n",
-    "        axs[1].set_ylim([0, 0.87])\n",
-    "    \n",
-    "        axs[0].text(-0.075, 1.1, \"(c)\", transform=axs[0].transAxes,\n",
-    "          fontsize=10, va='top', ha='right')\n",
+    "evidential = df[\"evidential\"]\n",
     "\n",
-    "        axs[1].text(-0.05, 1.1, \"(d)\", transform=axs[1].transAxes,\n",
-    "          fontsize=10, va='top', ha='right')\n",
-    "    \n",
-    "    # make it pretty\n",
-    "    plt.tight_layout()\n",
     "\n",
-    "    if save_location:\n",
-    "        plt.savefig(\n",
-    "            os.path.join(save_location, \"compare_uncertanties.pdf\"),\n",
-    "            dpi=300,\n",
-    "            bbox_inches=\"tight\",\n",
-    "        )\n",
-    "        \n",
-    "    else:\n",
-    "        return fig"
+    "true_labels_det = df_det[\"true_label\"].values\n",
+    "pred_labels_det = df_det[\"pred_label\"]\n",
+    "pred_probs_det = df_det[[f\"pred_conf{k+1}\" for k in range(4)]].values"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "c61111f1-063a-4784-a6ec-c189212ab5e4",
+   "execution_count": 12,
+   "id": "0d97c3a4-6c5f-438f-bc69-e23015f73073",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0 151518.0\n",
-      "0.0 55348.0\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "fig1 = plot_uncertainties(\n",
-    "    [df_det, df],\n",
-    "    [\"aleatoric\"],\n",
-    "    [\"epistemic\"],\n",
-    "    x_labels=[r\"Top-1 epistemic $\\sigma$\"],\n",
-    "    y_labels=[r\"Top-1 aleatoric $\\sigma$\"],\n",
-    "    num_bins=50,\n",
-    "    #save_location=save_location\n",
-    ")"
+    "fig, axes = plt.subplots(2, 4, figsize=(10, 5), sharex=True, sharey=True)\n",
+    "bins = np.arange(0, 1.1, 0.1)\n",
+    "bin_centers = 0.5 * (bins[:-1] + bins[1:])\n",
+    "labels = [\"Rain\", \"Snow\", \"Sleet\", \"Freezing Rain\"]\n",
+    "for true_label in range(4):\n",
+    "    true_match = np.where(true_labels == true_label, 1, 0)\n",
+    "    prob_label = pred_probs[:, true_label]\n",
+    "    obs_rel_freq = np.zeros(bins.size - 1)\n",
+    "    pred_freq = np.zeros(bins.size - 1)\n",
+    "    climo = true_match.sum() / true_match.size\n",
+    "    all_points = np.sort(np.concatenate([bins, [climo]]))\n",
+    "    no_skill = 0.5 * all_points + 0.5 * climo\n",
+    "    for b, bin_start in enumerate(bins[:-1]):\n",
+    "        valid_preds = np.logical_and(prob_label >= bin_start, prob_label < bins[b + 1])\n",
+    "        pred_freq[b] = np.sum(valid_preds) \n",
+    "        obs_rel_freq[b] = np.sum(true_match[valid_preds] == 1) / pred_freq[b]\n",
+    "    rel_obj = DistributedReliability(thresholds=bins)\n",
+    "    rel_obj.update(prob_label, true_match)\n",
+    "    bss = rel_obj.brier_skill_score()\n",
+    "    bs_rel, bs_res, bs_unc = rel_obj.brier_score_components()\n",
+    "    bs_rel /= bs_unc\n",
+    "    bs_res /= bs_unc\n",
+    "    axes[0, true_label].plot(bins, bins, 'k-.')\n",
+    "    axes[0, true_label].plot(bins, np.ones(bins.size) * climo, 'k-.')\n",
+    "    axes[0, true_label].plot(np.ones(bins.size) * climo, bins, 'k-.')\n",
+    "    above_climo = all_points >= climo\n",
+    "    below_climo = all_points <= climo\n",
+    "    axes[0, true_label].fill_between(all_points[above_climo], np.ones(above_climo.sum()), no_skill[above_climo], color='skyblue', zorder=0)\n",
+    "    axes[0, true_label].fill_between(all_points[below_climo], np.zeros(below_climo.sum()), no_skill[below_climo], color='skyblue', zorder=0) \n",
+    "    sc = axes[0, true_label].scatter(bin_centers, obs_rel_freq, 50, pred_freq / pred_probs.shape[0], marker=\"s\", \n",
+    "                                cmap=\"Reds\", edgecolor='k', zorder=100, norm=LogNorm(vmin=0.01, vmax=1))\n",
+    "    axes[0, true_label].set_xlim(0, 1)\n",
+    "    axes[0, true_label].set_ylim(0, 1)\n",
+    "    axes[0, true_label].set_title(labels[true_label])\n",
+    "    axes[0, true_label].set_xticks(np.arange(0, 1.2, 0.2))\n",
+    "    axes[0, true_label].text(0.6, 0.1, f\"BSS: {bss:0.3f}\\nRel: {bs_rel:0.3f}\\nRes: {bs_res:0.3f}\", fontsize=8, \n",
+    "                             bbox=dict(boxstyle=\"round\", fc=\"0.8\", ec=\"k\"))\n",
+    "\n",
+    "    true_match = np.where(true_labels_det == true_label, 1, 0)\n",
+    "    prob_label = pred_probs_det[:, true_label]\n",
+    "    obs_rel_freq = np.zeros(bins.size - 1)\n",
+    "    pred_freq = np.zeros(bins.size - 1)\n",
+    "    climo = true_match.sum() / true_match.size\n",
+    "    all_points = np.sort(np.concatenate([bins, [climo]]))\n",
+    "    no_skill = 0.5 * all_points + 0.5 * climo\n",
+    "    for b, bin_start in enumerate(bins[:-1]):\n",
+    "        valid_preds = np.logical_and(prob_label >= bin_start, prob_label < bins[b + 1])\n",
+    "        pred_freq[b] = np.sum(valid_preds) \n",
+    "        obs_rel_freq[b] = np.sum(true_match[valid_preds] == 1) / pred_freq[b]\n",
+    "    axes[1, true_label].plot(bins, bins, 'k-.')\n",
+    "    axes[1, true_label].plot(bins, np.ones(bins.size) * climo, 'k-.')\n",
+    "    axes[1, true_label].plot(np.ones(bins.size) * climo, bins, 'k-.')\n",
+    "    above_climo = all_points >= climo\n",
+    "    below_climo = all_points <= climo\n",
+    "    axes[1, true_label].fill_between(all_points[above_climo], np.ones(above_climo.sum()), no_skill[above_climo], color='skyblue', zorder=0)\n",
+    "    axes[1, true_label].fill_between(all_points[below_climo], np.zeros(below_climo.sum()), no_skill[below_climo], color='skyblue', zorder=0) \n",
+    "    sc = axes[1, true_label].scatter(bin_centers, obs_rel_freq, 50, pred_freq / pred_probs.shape[0], marker=\"s\", \n",
+    "                                cmap=\"Reds\", edgecolor='k', zorder=100, norm=LogNorm(vmin=0.01, vmax=1))\n",
+    "    rel_obj = DistributedReliability(thresholds=bins)\n",
+    "    rel_obj.update(prob_label, true_match)\n",
+    "    bss = rel_obj.brier_skill_score()\n",
+    "    bs_rel, bs_res, bs_unc = rel_obj.brier_score_components()\n",
+    "    bs_rel /= bs_unc\n",
+    "    bs_res /= bs_unc\n",
+    "    axes[1, true_label].text(0.6, 0.1, f\"BSS: {bss:0.3f}\\nRel: {bs_rel:0.3f}\\nRes: {bs_res:0.3f}\", fontsize=8, \n",
+    "                             bbox=dict(boxstyle=\"round\", fc=\"0.8\", ec=\"k\"))\n",
+    "    axes[1, true_label].set_xlim(0, 1)\n",
+    "    axes[1, true_label].set_ylim(0, 1)\n",
+    "    axes[1, true_label].set_title(labels[true_label])\n",
+    "    axes[1, true_label].set_xticks(np.arange(0, 1.2, 0.2))\n",
+    "    axes[1, true_label].set_xlabel(\"P-Type Probability\")\n",
+    "axes[0, 0].set_ylabel(\"Evidential\\nObserved Rel. Frequency\")\n",
+    "axes[1, 0].set_ylabel(\"Deterministic\\nObserved Rel. Frequency\")\n",
+    "axes[0, 0].text(-0.25, 1.1, \"(a)\", transform=axes[0, 0].transAxes,\n",
+    "      fontsize=12, va='top', ha='right')\n",
+    "axes[1, 0].text(-0.25, 1.1, \"(b)\", transform=axes[1, 0].transAxes,\n",
+    "      fontsize=12, va='top', ha='right')\n",
+    "cbaxes = fig.add_axes([0.1, -0.02, 0.8, 0.03])  # left, bottom, width, height]\n",
+    "cbar = fig.colorbar(sc, cax=cbaxes, orientation=\"horizontal\", pad = 0.01)\n",
+    "cbar.set_label(\"$\\log_{10}$ Prediction Relative Frequency\")\n",
+    "plt.savefig(\"evidential_attributes_diagram_unweighted.png\", dpi=300, bbox_inches=\"tight\")\n",
+    "plt.savefig(\"evidential_attributes_diagram_unweighted.pdf\", dpi=300, bbox_inches=\"tight\")"
    ]
   },
   {
@@ -697,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "id": "eed4ca85-5d1d-424a-9d07-6cc315ee26e8",
    "metadata": {},
    "outputs": [],
@@ -1000,174 +643,44 @@
    "cell_type": "code",
    "execution_count": 18,
    "id": "6d3aac7f-e785-468c-92ce-a6cd815252e5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "classifier_attribution(\n",
-    "    true_labels,\n",
-    "    pred_probs\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "47c12bd1-6454-43d6-9b1d-63f980893b5f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "classifier_attribution_compare(\n",
-    "    [df_det[\"true_label\"], df[\"true_label\"]],\n",
-    "    [df_det[[f\"pred_conf{k+1}\" for k in range(4)]].values, df[[f\"pred_conf{k+1}\" for k in range(4)]].values]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "81aa8dea-cf6c-47d8-bf8a-8544e1985556",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "true_labels = df[\"true_label\"].values\n",
-    "pred_labels = df[\"pred_label\"]\n",
-    "pred_probs = df[[f\"pred_conf{k+1}\" for k in range(4)]].values\n",
-    "confidences = df[\"pred_conf\"]\n",
-    "\n",
-    "evidential = df[\"evidential\"]\n",
-    "#total = np.sqrt(df[\"epistemic\"] + df[\"aleatoric\"])\n",
-    "#epistemic = np.sqrt(df[\"epistemic\"])\n",
-    "#aleatoric = np.sqrt(df[\"aleatoric\"])\n",
-    "\n",
-    "true_labels_det = df_det[\"true_label\"].values\n",
-    "pred_labels_det = df_det[\"pred_label\"]\n",
-    "pred_probs_det = df_det[[f\"pred_conf{k+1}\" for k in range(4)]].values\n",
-    "#confidences_det = df_det[\"pred_conf\"]\n",
-    "\n",
-    "#evidential_det = df_det[\"entropy\"]\n",
-    "#total_det = np.sqrt(df_det[\"epistemic\"] + df[\"aleatoric\"])\n",
-    "#epistemic_det = np.sqrt(df_det[\"epistemic\"])\n",
-    "#aleatoric_det = np.sqrt(df_det[\"aleatoric\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "ec33a978-7e1c-44ef-836d-ccab156bb3f5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(2, 4, figsize=(10, 5), sharex=True, sharey=True)\n",
-    "bins = np.arange(0, 1.1, 0.1)\n",
-    "bin_centers = 0.5 * (bins[:-1] + bins[1:])\n",
-    "labels = [\"Rain\", \"Snow\", \"Sleet\", \"Frz Rain\"]\n",
-    "for true_label in range(4):\n",
-    "    true_match = np.where(true_labels == true_label, 1, 0)\n",
-    "    prob_label = pred_probs[:, true_label]\n",
-    "    obs_rel_freq = np.zeros(bins.size - 1)\n",
-    "    pred_freq = np.zeros(bins.size - 1)\n",
-    "    climo = true_match.sum() / true_match.size\n",
-    "    all_points = np.sort(np.concatenate([bins, [climo]]))\n",
-    "    no_skill = 0.5 * all_points + 0.5 * climo\n",
-    "    for b, bin_start in enumerate(bins[:-1]):\n",
-    "        valid_preds = np.logical_and(prob_label >= bin_start, prob_label < bins[b + 1])\n",
-    "        pred_freq[b] = np.sum(valid_preds) \n",
-    "        obs_rel_freq[b] = np.sum(true_match[valid_preds] == 1) / pred_freq[b]\n",
-    "    rel_obj = DistributedReliability(thresholds=bins)\n",
-    "    rel_obj.update(prob_label, true_match)\n",
-    "    bss = rel_obj.brier_skill_score()\n",
-    "    bs_rel, bs_res, bs_unc = rel_obj.brier_score_components()\n",
-    "    bs_rel /= bs_unc\n",
-    "    bs_res /= bs_unc\n",
-    "    axes[0, true_label].plot(bins, bins, 'k-.')\n",
-    "    axes[0, true_label].plot(bins, np.ones(bins.size) * climo, 'k-.')\n",
-    "    axes[0, true_label].plot(np.ones(bins.size) * climo, bins, 'k-.')\n",
-    "    above_climo = all_points >= climo\n",
-    "    below_climo = all_points <= climo\n",
-    "    axes[0, true_label].fill_between(all_points[above_climo], np.ones(above_climo.sum()), no_skill[above_climo], color='skyblue', zorder=0)\n",
-    "    axes[0, true_label].fill_between(all_points[below_climo], np.zeros(below_climo.sum()), no_skill[below_climo], color='skyblue', zorder=0) \n",
-    "    sc = axes[0, true_label].scatter(bin_centers, obs_rel_freq, 50, pred_freq / pred_probs.shape[0], marker=\"s\", \n",
-    "                                cmap=\"Reds\", edgecolor='k', zorder=100, norm=LogNorm(vmin=0.01, vmax=1))\n",
-    "    axes[0, true_label].set_xlim(0, 1)\n",
-    "    axes[0, true_label].set_ylim(0, 1)\n",
-    "    axes[0, true_label].set_title(labels[true_label])\n",
-    "    axes[0, true_label].set_xticks(np.arange(0, 1.2, 0.2))\n",
-    "    axes[0, true_label].text(0.6, 0.1, f\"BSS: {bss:0.3f}\\nRel: {bs_rel:0.3f}\\nRes: {bs_res:0.3f}\", fontsize=8, \n",
-    "                             bbox=dict(boxstyle=\"round\", fc=\"0.8\", ec=\"k\"))\n",
-    "\n",
-    "    true_match = np.where(true_labels_det == true_label, 1, 0)\n",
-    "    prob_label = pred_probs_det[:, true_label]\n",
-    "    obs_rel_freq = np.zeros(bins.size - 1)\n",
-    "    pred_freq = np.zeros(bins.size - 1)\n",
-    "    climo = true_match.sum() / true_match.size\n",
-    "    all_points = np.sort(np.concatenate([bins, [climo]]))\n",
-    "    no_skill = 0.5 * all_points + 0.5 * climo\n",
-    "    for b, bin_start in enumerate(bins[:-1]):\n",
-    "        valid_preds = np.logical_and(prob_label >= bin_start, prob_label < bins[b + 1])\n",
-    "        pred_freq[b] = np.sum(valid_preds) \n",
-    "        obs_rel_freq[b] = np.sum(true_match[valid_preds] == 1) / pred_freq[b]\n",
-    "    axes[1, true_label].plot(bins, bins, 'k-.')\n",
-    "    axes[1, true_label].plot(bins, np.ones(bins.size) * climo, 'k-.')\n",
-    "    axes[1, true_label].plot(np.ones(bins.size) * climo, bins, 'k-.')\n",
-    "    above_climo = all_points >= climo\n",
-    "    below_climo = all_points <= climo\n",
-    "    axes[1, true_label].fill_between(all_points[above_climo], np.ones(above_climo.sum()), no_skill[above_climo], color='skyblue', zorder=0)\n",
-    "    axes[1, true_label].fill_between(all_points[below_climo], np.zeros(below_climo.sum()), no_skill[below_climo], color='skyblue', zorder=0) \n",
-    "    sc = axes[1, true_label].scatter(bin_centers, obs_rel_freq, 50, pred_freq / pred_probs.shape[0], marker=\"s\", \n",
-    "                                cmap=\"Reds\", edgecolor='k', zorder=100, norm=LogNorm(vmin=0.01, vmax=1))\n",
-    "    rel_obj = DistributedReliability(thresholds=bins)\n",
-    "    rel_obj.update(prob_label, true_match)\n",
-    "    bss = rel_obj.brier_skill_score()\n",
-    "    bs_rel, bs_res, bs_unc = rel_obj.brier_score_components()\n",
-    "    bs_rel /= bs_unc\n",
-    "    bs_res /= bs_unc\n",
-    "    axes[1, true_label].text(0.6, 0.1, f\"BSS: {bss:0.3f}\\nRel: {bs_rel:0.3f}\\nRes: {bs_res:0.3f}\", fontsize=8, \n",
-    "                             bbox=dict(boxstyle=\"round\", fc=\"0.8\", ec=\"k\"))\n",
-    "    axes[1, true_label].set_xlim(0, 1)\n",
-    "    axes[1, true_label].set_ylim(0, 1)\n",
-    "    axes[1, true_label].set_title(labels[true_label])\n",
-    "    axes[1, true_label].set_xticks(np.arange(0, 1.2, 0.2))\n",
-    "    axes[1, true_label].set_xlabel(\"P-Type Probability\")\n",
-    "axes[0, 0].set_ylabel(\"Evidential\\nObserved Rel. Frequency\")\n",
-    "axes[1, 0].set_ylabel(\"Deterministic\\nObserved Rel. Frequency\")\n",
-    "cbaxes = fig.add_axes([0.1, -0.02, 0.8, 0.03])  # left, bottom, width, height]\n",
-    "cbar = fig.colorbar(sc, cax=cbaxes, orientation=\"horizontal\", pad = 0.01)\n",
-    "cbar.set_label(\"$\\log_{10}$ Prediction Relative Frequency\")\n",
-    "plt.savefig(\"evidential_attributes_diagram_unweighted.png\", dpi=300, bbox_inches=\"tight\")\n",
-    "plt.savefig(\"evidential_attributes_diagram_unweighted.pdf\", dpi=300, bbox_inches=\"tight\")"
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "classifier_attribution(\n",
+    "    true_labels,\n",
+    "    pred_probs\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "81aa8dea-cf6c-47d8-bf8a-8544e1985556",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true_labels = df[\"true_label\"].values\n",
+    "pred_labels = df[\"pred_label\"]\n",
+    "pred_probs = df[[f\"pred_conf{k+1}\" for k in range(4)]].values\n",
+    "confidences = df[\"pred_conf\"]\n",
+    "\n",
+    "evidential = df[\"evidential\"]\n",
+    "\n",
+    "\n",
+    "true_labels_det = df_det[\"true_label\"].values\n",
+    "pred_labels_det = df_det[\"pred_label\"]\n",
+    "pred_probs_det = df_det[[f\"pred_conf{k+1}\" for k in range(4)]].values"
    ]
   },
   {
@@ -1656,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 14,
    "id": "adc74d37-d63e-4a79-b86a-64f6ba2593e2",
    "metadata": {},
    "outputs": [],
@@ -2321,30 +1834,16 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "b30448b1-f4f5-4709-b8d5-461a09be2773",
+   "cell_type": "markdown",
+   "id": "97895a61-4362-4424-8b0e-af02e7f3aa32",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.022402905936059432 0.03033074307555186 0.26137958835181563\n"
-     ]
-    }
-   ],
    "source": [
-    "true_lab = np.where(df[\"true_label\"] == 3, 1, 0)\n",
-    "bs = np.mean((df[\"pred_conf4\"] - true_lab) ** 2)\n",
-    "bs_c = np.mean((true_lab - true_lab.mean()) ** 2)\n",
-    "bss = 1 - bs / bs_c\n",
-    "print(bs, bs_c, bss)"
+    "# Discard Fraction (Figure 4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "b0b6f5b6-31f0-45ac-9199-d54467bab79e",
    "metadata": {},
    "outputs": [],
@@ -2358,28 +1857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "id": "0e622c08-02de-4b4b-b6aa-8597582e58a0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.any(np.isnan(df[\"true_label\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 19,
    "id": "4f04ad80-0698-41be-b67f-abb17c5ad321",
    "metadata": {},
    "outputs": [
@@ -2411,10 +1889,10 @@
     "bss_drop_dict = {}\n",
     "for uq_metric in uq_metrics:\n",
     "    print(uq_metric)\n",
+    "    uq_percentiles = np.percentile(df[uq_metric], drop_percentiles)\n",
     "    bs_drop_dict[uq_metric] = np.zeros((uq_percentiles.shape[0], 4)) \n",
     "    bs_c_drop_dict[uq_metric] = np.zeros((uq_percentiles.shape[0], 4))\n",
     "    bss_drop_dict[uq_metric] = np.zeros((uq_percentiles.shape[0], 4))\n",
-    "    uq_percentiles = np.percentile(df[uq_metric], drop_percentiles)\n",
     "    print(uq_percentiles)\n",
     "    for u, uqp in enumerate(uq_percentiles):\n",
     "        drop = df[uq_metric] <= uqp\n",
@@ -2427,7 +1905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 40,
    "id": "05ed552f-a6fe-4e43-84e6-055137dcfe98",
    "metadata": {},
    "outputs": [
@@ -2458,10 +1936,10 @@
     "bss_drop_dict_e = {}\n",
     "for uq_metric in uq_metrics:\n",
     "    print(uq_metric)\n",
+    "    uq_percentiles = np.percentile(df_ens_stats[uq_metric], drop_percentiles)\n",
     "    bs_drop_dict_e[uq_metric] = np.zeros((uq_percentiles.shape[0], 4)) \n",
     "    bs_c_drop_dict_e[uq_metric] = np.zeros((uq_percentiles.shape[0], 4))\n",
     "    bss_drop_dict_e[uq_metric] = np.zeros((uq_percentiles.shape[0], 4))\n",
-    "    uq_percentiles = np.percentile(df_ens_stats[uq_metric], drop_percentiles)\n",
     "    print(uq_percentiles)\n",
     "    for u, uqp in enumerate(uq_percentiles):\n",
     "        drop = df_ens_stats[uq_metric] <= uqp\n",
@@ -2474,13 +1952,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 41,
    "id": "30606467-d6d1-4757-818d-442910a0c0a1",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x400 with 4 Axes>"
       ]
@@ -2499,9 +1977,9 @@
     "fig, axes = plt.subplots(1, 4, figsize=(10, 4), sharey=True)\n",
     "for a, ax in enumerate(axes):\n",
     "    for i in range(4):\n",
-    "        ax.plot(np.arange(0, 100, 10), bs_drop_dict[uq_metrics[a]][:, i], marker=markers[i], color=colors[i], label=labels[i])\n",
+    "        ax.plot(np.arange(0, 100, 10), bs_drop_dict[uq_metrics[a]][:, i], lw=0.8, ms=5, marker=markers[i], color=colors[i], label=labels[i])\n",
     "        if a < 3:\n",
-    "            ax.plot(np.arange(0, 100, 10), bs_drop_dict_e[uq_metrics[a]][:, i], marker=markers[i], color=colors[i], linestyle=\"-.\")\n",
+    "            ax.plot(np.arange(0, 100, 10), bs_drop_dict_e[uq_metrics[a]][:, i], lw=0.8, ms=5, marker=markers[i], color=colors[i], linestyle=\"-.\")\n",
     "    di_ev = np.mean(bs_drop_dict[uq_metrics[a]][:-1] - bs_drop_dict[uq_metrics[a]][1:])\n",
     "    di_text = f\"Discard\\nImprovement\\nEvi: {di_ev:0.5f}\\n\"\n",
     "    if a < 3:\n",
@@ -2509,8 +1987,8 @@
     "        di_text += f\"Ens: {di_ens:0.5f}\"\n",
     "    #ax.set_yscale(\"log\")\n",
     "    ax.text(20, 0.022, di_text, fontsize=8, bbox=dict(boxstyle=\"round\", fc=\"0.8\", ec=\"k\"))\n",
-    "    ax.set_xticks(np.arange(0, 60, 10))\n",
-    "    ax.set_xlim(0, 50)\n",
+    "    ax.set_xticks(np.arange(0, 100, 10))\n",
+    "    #ax.set_xlim(0, 50)\n",
     "    ax.set_xlabel(\"Percent Discarded\")\n",
     "    if a == 0:\n",
     "        ax.set_ylabel(\"Brier Score\")\n",
@@ -2522,25 +2000,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
-   "id": "b56d0587-3c64-4ac6-a21f-0691c3520777",
+   "execution_count": 33,
+   "id": "ca9e8b13-57f0-4efa-95f6-48a960cab5fe",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x14599f7908e0>"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1000x400 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -2548,40 +2016,55 @@
     }
    ],
    "source": [
-    "fig, ax= plt.subplots(1, 1)\n",
-    "for i in range(4):\n",
-    "    plt.plot(np.arange(0, 100, 10), bs_drop_dict[\"epi_sum\"][:, i] / bs_drop_dict[\"ale_sum\"][:, i], marker=markers[i], color=colors[i], label=labels[i])\n",
-    "    #plt.plot(np.arange(0, 100, 10), bs_drop_dict_e[\"epi_sum\"][:, i] / bs_drop_dict_e[\"ale_sum\"][:, i], marker=markers[i], color=colors[i], linestyle=\"-.\")\n",
-    "ax.set_xticks(np.arange(0, 60, 10))\n",
-    "ax.set_xlim(0, 50)\n",
-    "ax.set_xlabel(\"Percent Discarded\")\n",
-    "ax.set_ylabel(\"BS$_{epi}$/BS$_{ale}$\")\n",
-    "ax.set_title(\"Evidential Discard Fraction Ratio\")\n",
-    "ax.legend(fontsize=8)"
+    "colors=[\"m\", \"b\", \"y\", \"c\"]\n",
+    "markers = [\"o\", \"s\", \"p\", \"^\"]\n",
+    "labels = [\"Rain\", \"Snow\", \"Sleet\", \"Frz Rain\"]\n",
+    "uq_metrics = [\"ale_sum\", \"epi_sum\", \"total_sum\", \"evidential\"]\n",
+    "uq_titles = [\"Aleatoric\", \"Epistemic\", \"Total\", \"DST $u$\"]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 4, figsize=(10, 4), sharey=True)\n",
+    "for a, ax in enumerate(axes):\n",
+    "    for i in range(4):\n",
+    "        ax.plot(np.arange(5, 95, 10), bs_drop_dict[uq_metrics[a]][:-1, i] - bs_drop_dict[uq_metrics[a]][1:, i], marker=markers[i], color=colors[i], label=labels[i])\n",
+    "        if a < 3:\n",
+    "            ax.plot(np.arange(5, 95, 10), bs_drop_dict_e[uq_metrics[a]][:-1, i] - bs_drop_dict_e[uq_metrics[a]][1:, i], marker=markers[i], color=colors[i], linestyle=\"-.\")\n",
+    "    di_ev = np.mean(bs_drop_dict[uq_metrics[a]][:-1] - bs_drop_dict[uq_metrics[a]][1:])\n",
+    "    di_text = f\"Discard\\nImprovement\\nEvi: {di_ev:0.5f}\\n\"\n",
+    "    if a < 3:\n",
+    "        di_ens = np.mean(bs_drop_dict_e[uq_metrics[a]][:-1] - bs_drop_dict_e[uq_metrics[a]][1:])\n",
+    "        di_text += f\"Ens: {di_ens:0.5f}\"\n",
+    "    #ax.set_yscale(\"log\")\n",
+    "    ax.text(20, 0.022, di_text, fontsize=8, bbox=dict(boxstyle=\"round\", fc=\"0.8\", ec=\"k\"))\n",
+    "    ax.set_xticks(np.arange(0, 60, 10))\n",
+    "    #ax.set_xlim(0, 50)\n",
+    "    ax.set_xlabel(\"Percent Discarded\")\n",
+    "    if a == 0:\n",
+    "        ax.set_ylabel(\"Brier Score\")\n",
+    "    ax.set_title(uq_titles[a])\n",
+    "    ax.legend(fontsize=8)\n",
+    "plt.savefig(\"evidential_discard_slope.png\", dpi=300, bbox_inches=\"tight\")\n",
+    "plt.savefig(\"evidential_discard_slope.pdf\", dpi=300, bbox_inches=\"tight\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
-   "id": "36519942-1f24-43f7-a895-bbf2b3fc0f63",
+   "execution_count": 138,
+   "id": "b56d0587-3c64-4ac6-a21f-0691c3520777",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1459b7e6ecb0>,\n",
-       " <matplotlib.lines.Line2D at 0x1459b7e6eb30>,\n",
-       " <matplotlib.lines.Line2D at 0x1459b7e6e680>,\n",
-       " <matplotlib.lines.Line2D at 0x1459b7e6d420>]"
+       "<matplotlib.legend.Legend at 0x14599f7908e0>"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2591,171 +2074,24 @@
     }
    ],
    "source": [
-    "plt.plot(bs_drop_dict_e[\"epi_sum\"][:-1] - bs_drop_dict_e[\"epi_sum\"][1:])\n",
-    "plt.plot(bs_drop_dict_e[\"ale_sum\"][:-1] - bs_drop_dict_e[\"ale_sum\"][1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "id": "c577c3c0-9eaf-4c29-890b-f483e0d1786c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.002723550734134988"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(bs_drop_dict_e[\"epi_sum\"][:-1] - bs_drop_dict_e[\"epi_sum\"][1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "id": "9f0f9aba-1a41-4063-bbe6-4551debe9507",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.002636072039850259"
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(bs_drop_dict[\"epi_sum\"][:-1] - bs_drop_dict[\"epi_sum\"][1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "id": "c5e49062-a538-462d-975e-10c40c7d46aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.002608063875100483"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(bs_drop_dict[\"ale_sum\"][:-1] - bs_drop_dict[\"ale_sum\"][1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "id": "2df8596b-7ae9-411b-b4fd-417aef1b0850",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.002459482158080423"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(bs_drop_dict_e[\"ale_sum\"][:-1] - bs_drop_dict_e[\"ale_sum\"][1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "9655b16d-98ac-4efe-9ba1-1d0fbc95c7e8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.002463441086678418"
-      ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(bs_drop_dict_e[\"total_sum\"][:-1] - bs_drop_dict_e[\"total_sum\"][1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "id": "724f004c-0a67-4857-890e-f62e64829cc8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.002607582886449422"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(bs_drop_dict[\"total_sum\"][:-1] - bs_drop_dict[\"total_sum\"][1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "61e782e9-2227-4caa-8261-3d166eab3aee",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.002710647009450648"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(bs_drop_dict[\"evidential\"][:-1] - bs_drop_dict[\"evidential\"][1:])"
+    "fig, ax= plt.subplots(1, 1)\n",
+    "for i in range(4):\n",
+    "    plt.plot(np.arange(0, 100, 10), bs_drop_dict[\"epi_sum\"][:, i] / bs_drop_dict[\"ale_sum\"][:, i], marker=markers[i], color=colors[i], label=labels[i])\n",
+    "    #plt.plot(np.arange(0, 100, 10), bs_drop_dict_e[\"epi_sum\"][:, i] / bs_drop_dict_e[\"ale_sum\"][:, i], marker=markers[i], color=colors[i], linestyle=\"-.\")\n",
+    "ax.set_xticks(np.arange(0, 60, 10))\n",
+    "ax.set_xlim(0, 50)\n",
+    "ax.set_xlabel(\"Percent Discarded\")\n",
+    "ax.set_ylabel(\"BS$_{epi}$/BS$_{ale}$\")\n",
+    "ax.set_title(\"Evidential Discard Fraction Ratio\")\n",
+    "ax.legend(fontsize=8)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "92ebfef2-5fb6-42fc-8f26-6afd4f9ca0ae",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:guess]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-guess-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2767,7 +2103,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/regression_example.ipynb b/notebooks/regression_example.ipynb
index f67f649..5d3cf15 100644
--- a/notebooks/regression_example.ipynb
+++ b/notebooks/regression_example.ipynb
@@ -6,9 +6,7 @@
    "source": [
     "# MILES-GUESS Regression Example Notebook\n",
     "\n",
-    "John Schreck, David John Gagne, Charlie Becker, Gabrielle Gantos, Dhamma Kimpara, Thomas Martin\n",
-    "- distinguish between authors and contributors\n",
-    "- add orchids for people?"
+    "John Schreck, David John Gagne, Charlie Becker, Gabrielle Gantos, Dhamma Kimpara, Thomas Martin"
    ]
   },
   {
@@ -23,28 +21,34 @@
     "1) Follow package installation steps in the miles-guess [ReadMe](https://github.com/ai2es/miles-guess/tree/main).\n",
     "2) Run the cells in this notebook in order.\n",
     "\n",
+    "#### Quick Intro to Surface Layer (SL)\n",
+    "\n",
+    "The Surface Layer (SL) problem attempts to predict energy fluxes from Earth's surface given near-surface atmospheric conditions. The dataset used in this example problem originates from the Royal Netherlands Meteorological Institute (KNMI) Cabauw Experimental Site for Atmospheric Research. The outgoing longwave radiation, converted to skin temperature, is derived from a device located approximately 200 meters from the flux tower.\n",
+    "\n",
     "#### Key Points\n",
     "\n",
     "* Evidential deep learning is capable of training a single model to simultaneously learn the problem and estimate the problem's aleatoric and epistemic uncertainties. See this paper here().\n",
     "* This notebook represents an example of evidential deep learning for a regression problem. Specifically, that regression problem\n",
     "* Data? (Make sure to mention the subsetting of the dataset for ptype)\n",
-    "* Parameters?\n",
-    "* What will user be prepared to do with miles-guess after running this notebook?\n",
-    "\n",
-    "\n",
-    "#### Notes I can add\n",
-    "* Here is where data comes in\n",
-    "* Here's where you can vary a parameter or not\n",
-    "* Defaults to parameters\n",
-    "* Limits to the parameters (only good for this timestep, only good for this seed, etc)\n",
-    "* Physical meaning to these parameters?"
+    "* What will user be prepared to do with miles-guess after running this notebook?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-03-12 17:54:32.060087: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+      "2024-03-12 17:54:32.110346: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2024-03-12 17:54:32.949343: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+     ]
+    }
+   ],
    "source": [
     "import os, tqdm, yaml\n",
     "import numpy as np\n",
@@ -55,8 +59,7 @@
     "from sklearn.model_selection import GroupShuffleSplit\n",
     "from sklearn.preprocessing import MinMaxScaler, RobustScaler\n",
     "\n",
-    "from mlguess.keras.models import GaussianRegressorDNN, EvidentialRegressorDNN\n",
-    "from mlguess.keras.models import BaseRegressor as RegressorDNN\n",
+    "from mlguess.keras.models import EvidentialRegressorDNN\n",
     "from mlguess.keras.callbacks import get_callbacks\n",
     "from mlguess.regression_uq import compute_results"
    ]
@@ -67,7 +70,9 @@
    "source": [
     "## Config File\n",
     "\n",
-    "#### Load the config file"
+    "#### Parameters of interest\n",
+    "\n",
+    "#TODO: Highlight parameters of interest"
    ]
   },
   {
@@ -86,19 +91,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data\n",
-    "\n",
-    "#### Load Surface Layer data from the repo"
+    "## Data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = pd.read_csv(\"../data/sample_cabauw_surface_layer.csv\")\n",
-    "data[\"day\"] = data[\"Time\"].apply(lambda x: str(x).split(\" \")[0])"
+    "data = pd.read_csv(\"../data/sample_cabauw_surface_layer.csv\")"
    ]
   },
   {
@@ -107,28 +109,30 @@
    "source": [
     "#### Train-Valid-Test Splits\n",
     "\n",
-    "This is a two-step process:\n",
-    "1. Split all of the data on the day column between train (90%) and test (10%). The test data will be consisten accross all trained models and all data and model ensembles.\n",
-    "2. Split the 90% training data from Step 1 into training and validation."
+    "This is a three-step process:\n",
+    "1. Group data by day.\n",
+    "2. Split all of the data on the day column between train (90%) and test (10%). The test data will be consisten accross all trained models and all data and model ensembles.\n",
+    "3. Split the 90% training data from Step 1 into training and validation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO: Why do we need two seeds?\n",
-    "data_seed = 0\n",
-    "flat_seed = 1000"
+    "data[\"day\"] = data[\"Time\"].apply(lambda x: str(x).split(\" \")[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
+    "data_seed = 0\n",
+    "flat_seed = 1000\n",
+    "\n",
     "# Need the same test_data for all trained models (data and model ensembles)\n",
     "gsp = GroupShuffleSplit(n_splits=1, random_state=flat_seed, train_size=0.9)\n",
     "splits = list(gsp.split(data, groups=data[\"day\"]))\n",
@@ -151,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,49 +202,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-03-12 17:54:35.228747: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = EvidentialRegressorDNN(**conf[\"model\"])\n",
+    "model.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m  86/1795\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 588us/step - loss: 3.0557e-05  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/work/ggantos/conda-envs/guess/lib/python3.10/site-packages/keras/src/optimizers/base_optimizer.py:576: UserWarning: Gradients do not exist for variables ['bias', 'kernel', 'bias'] when minimizing the loss. If using `model.compile()`, did you forget to provide a `loss` argument?\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1795/1795\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 669us/step - loss: 2.9939e-05 - val_loss: 2.8053e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1460bc91e200>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "model = RegressorDNN(**conf[\"model\"])\n",
-    "model.build_neural_network(x_train.shape[-1], y_train.shape[-1])"
+    "model.fit(x_train,\n",
+    "          y_train,\n",
+    "          validation_data=(x_valid, y_valid))"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "model.fit(x_train,\n",
-    "          y_train,\n",
-    "          validation_data=(x_valid, y_valid),\n",
-    "          callbacks=get_callbacks(conf))"
+    "#### Predict with the model"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n"
+     ]
+    }
+   ],
    "source": [
-    "##### Predict with the model"
+    "y_pred = model.predict(x_test, return_uncertainties=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.06768488,  0.61546266,  1.7284483 ,  0.65492064],\n",
+       "       [-0.02590589,  0.7062391 ,  1.6261563 ,  0.66753787],\n",
+       "       [ 0.12891376,  0.7776666 ,  1.5624065 ,  0.73487395],\n",
+       "       ...,\n",
+       "       [-0.01874506,  0.7131814 ,  1.6728607 ,  0.7000923 ],\n",
+       "       [ 0.00279877,  0.73572874,  1.6917179 ,  0.7401652 ],\n",
+       "       [-0.07375085,  0.6940977 ,  1.7539929 ,  0.6484979 ]],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "y_pred = model.predict(x_test, y_scaler)"
+    "y_pred"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2545100680072519"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "mae = np.mean(np.abs(y_pred[:, 0]-test_data[output_cols[0]]))\n",
+    "mae = np.mean(np.abs(y_pred[:, 0] - test_data[output_cols[0]]))\n",
     "mae"
    ]
   },
@@ -248,25 +338,44 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Create a Monte Carlo ensemble"
+    "#### Create a Monte Carlo ensemble"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "monte_carlo_steps = 10"
+    "```yaml\n",
+    "ensemble:\n",
+    "    n_models: 1\n",
+    "    n_splits: 1\n",
+    "    monte_carlo_passes: 100\n",
+    "```\n",
+    "Create an ensemble of models (random initialization) using cross-validation splits. If MC passes > 0, an ensemble is created after each model finishes training on the test holdout. The LOTV may then be applied to the ensemble created from cross-validation. Otherwise a single ensemble is created but the LOTV is not applied."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'EvidentialRegressorDNN' object has no attribute 'predict_monte_carlo'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_monte_carlo\u001b[49m(x_test,\n\u001b[1;32m      2\u001b[0m                                     conf[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmonte_carlo_passes\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m      3\u001b[0m                                     y_scaler)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'EvidentialRegressorDNN' object has no attribute 'predict_monte_carlo'"
+     ]
+    }
+   ],
    "source": [
-    "results = model.predict_monte_carlo(x_test, monte_carlo_steps, y_scaler)"
+    "# TODO: will have a separate ensemble class implemented in the future\n",
+    "results = model.predict_monte_carlo(x_test,\n",
+    "                                    conf[\"monte_carlo_passes\"],\n",
+    "                                    y_scaler)"
    ]
   },
   {