fix: remove heuristic aleatoric split

Author: ooples

src/UncertaintyQuantification/BayesianNeuralNetworks/DeepEnsemble.cs

@@ -103,19 +103,19 @@ public UncertaintyPredictionResult<T, Tensor<T>> PredictWithUncertainty(Tensor<T
     /// <b>For Beginners:</b> If each model in the ensemble outputs both a prediction and
     /// its own uncertainty estimate (e.g., Gaussian likelihood), the average of individual
     /// model uncertainties represents aleatoric uncertainty (data noise).
+    ///
+    /// <b>Important:</b> This implementation does not currently have per-model variance heads, so a true
+    /// aleatoric/epistemic decomposition is not available. As a conservative default, this method returns
+    /// zeros; to estimate aleatoric uncertainty, modify ensemble members to output explicit variance (e.g.,
+    /// a Gaussian likelihood head).
     /// </remarks>
     public Tensor<T> EstimateAleatoricUncertainty(Tensor<T> input)
     {
         var totalUncertainty = PredictWithUncertainty(input).Variance ?? new Tensor<T>(input.Shape);
-
-        // In a basic ensemble, we approximate aleatoric as a fraction of total variance
-        // In practice, each model could output its own variance estimate
         var aleatoric = new Tensor<T>(totalUncertainty.Shape);
-        var aleatoricFactor = _numOps.FromDouble(0.3);
-
-        for (int i = 0; i < totalUncertainty.Length; i++)
+        for (int i = 0; i < aleatoric.Length; i++)
         {
-            aleatoric[i] = _numOps.Multiply(totalUncertainty[i], aleatoricFactor);
+            aleatoric[i] = _numOps.Zero;
         }
 
         return aleatoric;
@@ -134,17 +134,7 @@ public Tensor<T> EstimateAleatoricUncertainty(Tensor<T> input)
     public Tensor<T> EstimateEpistemicUncertainty(Tensor<T> input)
     {
         var totalUncertainty = PredictWithUncertainty(input).Variance ?? new Tensor<T>(input.Shape);
-
-        // Ensemble disagreement primarily captures epistemic uncertainty
-        var epistemic = new Tensor<T>(totalUncertainty.Shape);
-        var epistemicFactor = _numOps.FromDouble(0.7);
-
-        for (int i = 0; i < totalUncertainty.Length; i++)
-        {
-            epistemic[i] = _numOps.Multiply(totalUncertainty[i], epistemicFactor);
-        }
-
-        return epistemic;
+        return totalUncertainty;
     }
 
     /// <summary>