diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index 0e2bfd740..5e0d185e8 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -236,8 +236,7 @@ VectorValues HybridBayesNet::optimize(const DiscreteValues &assignment) const {
 }
 
 /* ************************************************************************* */
-HybridValues HybridBayesNet::sample(VectorValues given, std::mt19937_64 *rng,
-                                    SharedDiagonal model) const {
+HybridValues HybridBayesNet::sample(VectorValues given, std::mt19937_64 *rng) const {
   DiscreteBayesNet dbn;
   for (size_t idx = 0; idx < size(); idx++) {
     if (factors_.at(idx)->isDiscrete()) {
@@ -250,26 +249,24 @@ HybridValues HybridBayesNet::sample(VectorValues given, std::mt19937_64 *rng,
   // 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, model);
+  VectorValues sample = gbn.sample(given, rng);
   return HybridValues(assignment, sample);
 }
 
 /* ************************************************************************* */
-HybridValues HybridBayesNet::sample(std::mt19937_64 *rng,
-                                    SharedDiagonal model) const {
+HybridValues HybridBayesNet::sample(std::mt19937_64 *rng) const {
   VectorValues given;
-  return sample(given, rng, model);
+  return sample(given, rng);
 }
 
 /* ************************************************************************* */
-HybridValues HybridBayesNet::sample(VectorValues given,
-                                    SharedDiagonal model) const {
-  return sample(given, &kRandomNumberGenerator, model);
+HybridValues HybridBayesNet::sample(VectorValues given) const {
+  return sample(given, &kRandomNumberGenerator);
 }
 
 /* ************************************************************************* */
-HybridValues HybridBayesNet::sample(SharedDiagonal model) const {
-  return sample(&kRandomNumberGenerator, model);
+HybridValues HybridBayesNet::sample() const {
+  return sample(&kRandomNumberGenerator);
 }
 
 /* ************************************************************************* */
diff --git a/gtsam/hybrid/HybridBayesNet.h b/gtsam/hybrid/HybridBayesNet.h
index d6809e036..4b39ace25 100644
--- a/gtsam/hybrid/HybridBayesNet.h
+++ b/gtsam/hybrid/HybridBayesNet.h
@@ -130,11 +130,9 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    *
    * @param given Values of missing variables.
    * @param rng The pseudo-random number generator.
-   * @param model Optional diagonal noise model to use in sampling.
    * @return HybridValues
    */
-  HybridValues sample(VectorValues given, std::mt19937_64 *rng,
-                      SharedDiagonal model = nullptr) const;
+  HybridValues sample(VectorValues given, std::mt19937_64 *rng) const;
 
   /**
    * @brief Sample using ancestral sampling.
@@ -144,28 +142,24 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    *   auto sample = bn.sample(&rng);
    *
    * @param rng The pseudo-random number generator.
-   * @param model Optional diagonal noise model to use in sampling.
    * @return HybridValues
    */
-  HybridValues sample(std::mt19937_64 *rng,
-                      SharedDiagonal model = nullptr) const;
+  HybridValues sample(std::mt19937_64 *rng) const;
 
   /**
    * @brief Sample from an incomplete BayesNet, use default rng.
    *
    * @param given Values of missing variables.
-   * @param model Optional diagonal noise model to use in sampling.
    * @return HybridValues
    */
-  HybridValues sample(VectorValues given, SharedDiagonal model = nullptr) const;
+  HybridValues sample(VectorValues given) const;
 
   /**
    * @brief Sample using ancestral sampling, use default rng.
    *
-   * @param model Optional diagonal noise model to use in sampling.
    * @return HybridValues
    */
-  HybridValues sample(SharedDiagonal model = nullptr) const;
+  HybridValues sample() const;
 
   /// Prune the Hybrid Bayes Net such that we have at most maxNrLeaves leaves.
   HybridBayesNet prune(size_t maxNrLeaves);