diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index 3b5ab5b800..ff2752bcbe 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -39,8 +39,7 @@ bool HybridBayesNet::equals(const This &bn, double tol) const {
 
 /* ************************************************************************* */
 DiscreteConditional::shared_ptr HybridBayesNet::discreteConditionals() const {
-  // The canonical decision tree factor which will get
-  // the discrete conditionals added to it.
+  // The joint discrete probability.
   DiscreteConditional discreteProbs;
 
   for (auto &&conditional : *this) {
@@ -152,7 +151,7 @@ void HybridBayesNet::updateDiscreteConditionals(
       // Convert pointer from conditional to factor
       auto discreteFactor =
           std::dynamic_pointer_cast<DecisionTreeFactor>(discrete);
-      // Apply prunerFunc to the underlying AlgebraicDecisionTree
+      // Apply prunerFunc to the underlying conditional
       DecisionTreeFactor::ADT prunedDiscreteFactor =
           discreteFactor->apply(prunerFunc(prunedDiscreteProbs, *conditional));
 
@@ -173,7 +172,7 @@ void HybridBayesNet::updateDiscreteConditionals(
 
 /* ************************************************************************* */
 HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) {
-  // Get the decision tree of only the discrete keys
+  // Get the joint distribution of only the discrete keys
   gttic_(HybridBayesNet_PruneDiscreteConditionals);
   DiscreteConditional::shared_ptr discreteConditionals =
       this->discreteConditionals();
diff --git a/gtsam/hybrid/HybridSmoother.cpp b/gtsam/hybrid/HybridSmoother.cpp
index 27d3f70fcb..afa8340d2b 100644
--- a/gtsam/hybrid/HybridSmoother.cpp
+++ b/gtsam/hybrid/HybridSmoother.cpp
@@ -97,7 +97,8 @@ HybridSmoother::addConditionals(const HybridGaussianFactorGraph &originalGraph,
   HybridGaussianFactorGraph graph(originalGraph);
   HybridBayesNet hybridBayesNet(originalHybridBayesNet);
 
-  // If we are not at the first iteration, means we have conditionals to add.
+  // If hybridBayesNet is not empty,
+  // it means we have conditionals to add to the factor graph.
   if (!hybridBayesNet.empty()) {
     // We add all relevant conditional mixtures on the last continuous variable
     // in the previous `hybridBayesNet` to the graph