diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index de940ec69..54129775f 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -8,7 +8,7 @@
 
 /**
  * @file   HybridBayesNet.cpp
- * @brief  A bayes net of Gaussian Conditionals indexed by discrete keys.
+ * @brief  A Bayes net of Gaussian Conditionals indexed by discrete keys.
  * @author Fan Jiang
  * @author Varun Agrawal
  * @author Shangjie Xue
@@ -56,7 +56,7 @@ std::function<double(const Assignment<Key> &, double)> prunerFunc(
     const DecisionTreeFactor &decisionTree,
     const HybridConditional &conditional) {
   // Get the discrete keys as sets for the decision tree
-  // and the gaussian mixture.
+  // and the Gaussian mixture.
   auto decisionTreeKeySet = DiscreteKeysAsSet(decisionTree.discreteKeys());
   auto conditionalKeySet = DiscreteKeysAsSet(conditional.discreteKeys());
 
@@ -65,7 +65,7 @@ std::function<double(const Assignment<Key> &, double)> prunerFunc(
                     double probability) -> double {
     // typecast so we can use this to get probability value
     DiscreteValues values(choices);
-    // Case where the gaussian mixture has the same
+    // Case where the Gaussian mixture has the same
     // discrete keys as the decision tree.
     if (conditionalKeySet == decisionTreeKeySet) {
       if (decisionTree(values) == 0) {
@@ -156,7 +156,7 @@ HybridBayesNet HybridBayesNet::prune(size_t maxNrLeaves) {
     if (conditional->isHybrid()) {
       GaussianMixture::shared_ptr gaussianMixture = conditional->asMixture();
 
-      // Make a copy of the gaussian mixture and prune it!
+      // Make a copy of the Gaussian mixture and prune it!
       auto prunedGaussianMixture =
           boost::make_shared<GaussianMixture>(*gaussianMixture);
       prunedGaussianMixture->prune(*decisionTree);
@@ -200,7 +200,7 @@ GaussianBayesNet HybridBayesNet::choose(
       gbn.push_back(gm(assignment));
 
     } else if (conditional->isContinuous()) {
-      // If continuous only, add gaussian conditional.
+      // If continuous only, add Gaussian conditional.
       gbn.push_back((conditional->asGaussian()));
 
     } else if (conditional->isDiscrete()) {
@@ -236,32 +236,32 @@ VectorValues HybridBayesNet::optimize(const DiscreteValues &assignment) const {
 }
 
 /* ************************************************************************* */
-HybridValues HybridBayesNet::sample(VectorValues given,
+HybridValues HybridBayesNet::sample(HybridValues& given,
                                     std::mt19937_64 *rng) const {
   DiscreteBayesNet dbn;
   for (const sharedConditional &conditional : *this) {
     if (conditional->isDiscrete()) {
-      // If conditional is discrete-only, we add to the discrete bayes net.
-      dbn.push_back(conditional->asDiscrete());
+      // If conditional is discrete-only, we add to the discrete Bayes net.
+      dbn.push_back(conditional->asDiscreteConditional());
     }
   }
   // Sample a discrete assignment.
-  DiscreteValues assignment = dbn.sample();
-  // Select the continuous bayes net corresponding to the assignment.
-  GaussianBayesNet gbn = this->choose(assignment);
-  // Sample from the gaussian bayes net.
-  VectorValues sample = gbn.sample(given, rng);
-  return HybridValues(assignment, sample);
+  const DiscreteValues assignment = dbn.sample(given.discrete());
+  // Select the continuous Bayes net corresponding to the assignment.
+  GaussianBayesNet gbn = choose(assignment);
+  // Sample from the Gaussian Bayes net.
+  VectorValues sample = gbn.sample(given.continuous(), rng);
+  return {assignment, sample};
 }
 
 /* ************************************************************************* */
 HybridValues HybridBayesNet::sample(std::mt19937_64 *rng) const {
-  VectorValues given;
+  HybridValues given;
   return sample(given, rng);
 }
 
 /* ************************************************************************* */
-HybridValues HybridBayesNet::sample(VectorValues given) const {
+HybridValues HybridBayesNet::sample(HybridValues& given) const {
   return sample(given, &kRandomNumberGenerator);
 }
 
diff --git a/gtsam/hybrid/HybridBayesNet.h b/gtsam/hybrid/HybridBayesNet.h
index 4b39ace25..3412aaf78 100644
--- a/gtsam/hybrid/HybridBayesNet.h
+++ b/gtsam/hybrid/HybridBayesNet.h
@@ -8,7 +8,7 @@
 
 /**
  * @file    HybridBayesNet.h
- * @brief   A bayes net of Gaussian Conditionals indexed by discrete keys.
+ * @brief   A Bayes net of Gaussian Conditionals indexed by discrete keys.
  * @author  Varun Agrawal
  * @author  Fan Jiang
  * @author  Frank Dellaert
@@ -43,7 +43,7 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
   /// @name Standard Constructors
   /// @{
 
-  /** Construct empty bayes net */
+  /** Construct empty Bayes net */
   HybridBayesNet() = default;
 
   /// @}
@@ -132,7 +132,7 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    * @param rng The pseudo-random number generator.
    * @return HybridValues
    */
-  HybridValues sample(VectorValues given, std::mt19937_64 *rng) const;
+  HybridValues sample(HybridValues& given, std::mt19937_64 *rng) const;
 
   /**
    * @brief Sample using ancestral sampling.
@@ -152,7 +152,7 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    * @param given Values of missing variables.
    * @return HybridValues
    */
-  HybridValues sample(VectorValues given) const;
+  HybridValues sample(HybridValues& given) const;
 
   /**
    * @brief Sample using ancestral sampling, use default rng.