diff --git a/examples/variational_inference/bayesian_neural_network_advi.ipynb b/examples/variational_inference/bayesian_neural_network_advi.ipynb
index 84ed5f84..ff18bfa0 100644
--- a/examples/variational_inference/bayesian_neural_network_advi.ipynb
+++ b/examples/variational_inference/bayesian_neural_network_advi.ipynb
@@ -186,8 +186,12 @@
     "    }\n",
     "\n",
     "    with pm.Model(coords=coords) as neural_network:\n",
-    "        ann_input = pm.Data(\"ann_input\", X_train, mutable=True)\n",
-    "        ann_output = pm.Data(\"ann_output\", Y_train, mutable=True)\n",
+    "        # Define minibatch variables\n",
+    "        minibatch_x, minibatch_y = pm.Minibatch(X_train, Y_train, batch_size=50)\n",
+    "\n",
+    "        # Define data variables using minibatches\n",
+    "        ann_input = pm.Data(\"ann_input\", minibatch_x, mutable=True, dims=(\"obs_id\", \"train_cols\"))\n",
+    "        ann_output = pm.Data(\"ann_output\", minibatch_y, mutable=True, dims=\"obs_id\")\n",
     "\n",
     "        # Weights from input to hidden layer\n",
     "        weights_in_1 = pm.Normal(\n",
@@ -212,7 +216,8 @@
     "            \"out\",\n",
     "            act_out,\n",
     "            observed=ann_output,\n",
-    "            total_size=Y_train.shape[0],  # IMPORTANT for minibatches\n",
+    "            total_size=X_train.shape[0],  # IMPORTANT for minibatches\n",
+    "            dims=\"obs_id\",\n",
     "        )\n",
     "    return neural_network\n",
     "\n",
diff --git a/examples/variational_inference/bayesian_neural_network_advi.myst.md b/examples/variational_inference/bayesian_neural_network_advi.myst.md
index 28201192..3cb7c325 100644
--- a/examples/variational_inference/bayesian_neural_network_advi.myst.md
+++ b/examples/variational_inference/bayesian_neural_network_advi.myst.md
@@ -131,8 +131,12 @@ def construct_nn():
     }
 
     with pm.Model(coords=coords) as neural_network:
-        ann_input = pm.Data("ann_input", X_train, mutable=True)
-        ann_output = pm.Data("ann_output", Y_train, mutable=True)
+        # Define minibatch variables
+        minibatch_x, minibatch_y = pm.Minibatch(X_train, Y_train, batch_size=50)
+
+        # Define data variables using minibatches
+        ann_input = pm.Data("ann_input", minibatch_x, mutable=True, dims=("obs_id", "train_cols"))
+        ann_output = pm.Data("ann_output", minibatch_y, mutable=True, dims="obs_id")
 
         # Weights from input to hidden layer
         weights_in_1 = pm.Normal(
@@ -157,7 +161,8 @@ def construct_nn():
             "out",
             act_out,
             observed=ann_output,
-            total_size=Y_train.shape[0],  # IMPORTANT for minibatches
+            total_size=X_train.shape[0],  # IMPORTANT for minibatches
+            dims="obs_id",
         )
     return neural_network