diff --git a/gtsam/hybrid/HybridGaussianConditional.cpp b/gtsam/hybrid/HybridGaussianConditional.cpp
index a7ed9e939..ef38237f2 100644
--- a/gtsam/hybrid/HybridGaussianConditional.cpp
+++ b/gtsam/hybrid/HybridGaussianConditional.cpp
@@ -51,14 +51,14 @@ HybridGaussianConditional::HybridGaussianConditional(
                  discreteParents, GetFactorValuePairs(conditionals)),
       BaseConditional(continuousFrontals.size()),
       conditionals_(conditionals) {
-  // Calculate logConstant_ as the minimum of the negative-log normalizers of
+  // Calculate negLogConstant_ as the minimum of the negative-log normalizers of
   // the conditionals, by visiting the decision tree:
-  logConstant_ = std::numeric_limits<double>::infinity();
+  negLogConstant_ = std::numeric_limits<double>::infinity();
   conditionals_.visit(
       [this](const GaussianConditional::shared_ptr &conditional) {
         if (conditional) {
-          this->logConstant_ =
-              std::min(this->logConstant_, conditional->negLogConstant());
+          this->negLogConstant_ =
+              std::min(this->negLogConstant_, conditional->negLogConstant());
         }
       });
 }
@@ -84,7 +84,7 @@ GaussianFactorGraphTree HybridGaussianConditional::asGaussianFactorGraphTree()
   auto wrap = [this](const GaussianConditional::shared_ptr &gc) {
     // First check if conditional has not been pruned
     if (gc) {
-      const double Cgm_Kgcm = gc->negLogConstant() - this->logConstant_;
+      const double Cgm_Kgcm = gc->negLogConstant() - this->negLogConstant_;
       // If there is a difference in the covariances, we need to account for
       // that since the error is dependent on the mode.
       if (Cgm_Kgcm > 0.0) {
@@ -155,8 +155,7 @@ void HybridGaussianConditional::print(const std::string &s,
     std::cout << "(" << formatter(dk.first) << ", " << dk.second << "), ";
   }
   std::cout << std::endl
-            << " logNormalizationConstant: " << -negLogConstant()
-            << std::endl
+            << " logNormalizationConstant: " << -negLogConstant() << std::endl
             << std::endl;
   conditionals_.print(
       "", [&](Key k) { return formatter(k); },
@@ -214,7 +213,7 @@ std::shared_ptr<HybridGaussianFactor> HybridGaussianConditional::likelihood(
       [&](const GaussianConditional::shared_ptr &conditional)
           -> GaussianFactorValuePair {
         const auto likelihood_m = conditional->likelihood(given);
-        const double Cgm_Kgcm = conditional->negLogConstant() - logConstant_;
+        const double Cgm_Kgcm = conditional->negLogConstant() - negLogConstant_;
         if (Cgm_Kgcm == 0.0) {
           return {likelihood_m, 0.0};
         } else {
diff --git a/gtsam/hybrid/HybridGaussianConditional.h b/gtsam/hybrid/HybridGaussianConditional.h
index d2b8982a3..9c70cf6cb 100644
--- a/gtsam/hybrid/HybridGaussianConditional.h
+++ b/gtsam/hybrid/HybridGaussianConditional.h
@@ -66,7 +66,7 @@ class GTSAM_EXPORT HybridGaussianConditional
   Conditionals conditionals_;  ///< a decision tree of Gaussian conditionals.
   ///< Negative-log of the normalization constant (log(\sqrt(|2πΣ|))).
   ///< Take advantage of the neg-log space so everything is a minimization
-  double logConstant_;
+  double negLogConstant_;
 
   /**
    * @brief Convert a HybridGaussianConditional of conditionals into
@@ -158,7 +158,7 @@ class GTSAM_EXPORT HybridGaussianConditional
    *
    * @return double
    */
-  inline double negLogConstant() const override { return logConstant_; }
+  inline double negLogConstant() const override { return negLogConstant_; }
 
   /**
    * Create a likelihood factor for a hybrid Gaussian conditional,
diff --git a/gtsam/hybrid/HybridGaussianFactorGraph.cpp b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
index 85a2de464..82828bb41 100644
--- a/gtsam/hybrid/HybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
@@ -233,13 +233,13 @@ continuousElimination(const HybridGaussianFactorGraph &factors,
 
 /* ************************************************************************ */
 /**
- * @brief Exponentiate (not necessarily normalized) log-values, normalize, and
- * then return as AlgebraicDecisionTree<Key>.
+ * @brief Exponentiate (not necessarily normalized) negative log-values,
+ * normalize, and then return as AlgebraicDecisionTree<Key>.
  *
  * @param logValues DecisionTree of (unnormalized) log values.
  * @return AlgebraicDecisionTree<Key>
  */
-static AlgebraicDecisionTree<Key> probabilitiesFromLogValues(
+static AlgebraicDecisionTree<Key> probabilitiesFromNegativeLogValues(
     const AlgebraicDecisionTree<Key> &logValues) {
   // Perform normalization
   double min_log = logValues.min();
@@ -271,7 +271,7 @@ discreteElimination(const HybridGaussianFactorGraph &factors,
           DecisionTree<Key, double>(gmf->factors(), logProbability);
 
       AlgebraicDecisionTree<Key> probabilities =
-          probabilitiesFromLogValues(logProbabilities);
+          probabilitiesFromNegativeLogValues(logProbabilities);
       dfg.emplace_shared<DecisionTreeFactor>(gmf->discreteKeys(),
                                              probabilities);
 
@@ -337,7 +337,7 @@ static std::shared_ptr<Factor> createDiscreteFactor(
   AlgebraicDecisionTree<Key> negLogProbabilities(
       DecisionTree<Key, double>(eliminationResults, negLogProbability));
   AlgebraicDecisionTree<Key> probabilities =
-      probabilitiesFromLogValues(negLogProbabilities);
+      probabilitiesFromNegativeLogValues(negLogProbabilities);
 
   return std::make_shared<DecisionTreeFactor>(discreteSeparator, probabilities);
 }