diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index eeacc929b8..d343decc8f 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -291,7 +291,7 @@ AlgebraicDecisionTree<Key> HybridBayesNet::modelSelection() const {
                                 std::numeric_limits<double>::max());
         }
 
-        // Compute the error for X* and the assignment
+        // Compute the error at the MLE point X* for the current assignment
         double error =
             this->error(HybridValues(mu, DiscreteValues(assignment)));
 
diff --git a/gtsam/hybrid/HybridGaussianFactorGraph.cpp b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
index 42402076ea..be94f42eee 100644
--- a/gtsam/hybrid/HybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
@@ -295,8 +295,9 @@ static std::shared_ptr<Factor> createDiscreteFactor(
     if (!factor) return 1.0;  // TODO(dellaert): not loving this.
 
     // Logspace version of:
-    // exp(-factor->error(kEmpty)) / conditional->normalizationConstant();
-    return -factor->error(kEmpty) - conditional->logNormalizationConstant();
+    // exp(-factor->error(kEmpty)) * conditional->normalizationConstant();
+    // We take negative of the logNormalizationConstant (1/k) to get k
+    return -factor->error(kEmpty) + (-conditional->logNormalizationConstant());
   };
 
   AlgebraicDecisionTree<Key> logProbabilities(
@@ -324,6 +325,7 @@ static std::shared_ptr<Factor> createGaussianMixtureFactor(
     if (factor) {
       auto hf = std::dynamic_pointer_cast<HessianFactor>(factor);
       if (!hf) throw std::runtime_error("Expected HessianFactor!");
+      // Add 2.0 term since the constant term will be premultiplied by 0.5
       hf->constantTerm() += 2.0 * conditional->logNormalizationConstant();
     }
     return factor;