diff --git a/gtsam/discrete/DiscreteBayesNet.cpp b/gtsam/discrete/DiscreteBayesNet.cpp
index 7d929f12c..8c04cb91c 100644
--- a/gtsam/discrete/DiscreteBayesNet.cpp
+++ b/gtsam/discrete/DiscreteBayesNet.cpp
@@ -75,7 +75,7 @@ DiscreteValues DiscreteBayesNet::sample(DiscreteValues result) const {
 // been pruned before. Another, possibly faster approach is branch and bound
 // search to find the K-best leaves and then create a single pruned conditional.
 DiscreteBayesNet DiscreteBayesNet::prune(
-    size_t maxNrLeaves, const std::optional<double>& deadModeThreshold,
+    size_t maxNrLeaves, const std::optional<double>& marginalThreshold,
     DiscreteValues* fixedValues) const {
   // Multiply into one big conditional. NOTE: possibly quite expensive.
   DiscreteConditional joint;
@@ -89,13 +89,13 @@ DiscreteBayesNet DiscreteBayesNet::prune(
   DiscreteValues deadModesValues;
   // If we have a dead mode threshold and discrete variables left after pruning,
   // then we run dead mode removal.
-  if (deadModeThreshold.has_value() && pruned.keys().size() > 0) {
+  if (marginalThreshold.has_value() && pruned.keys().size() > 0) {
     DiscreteMarginals marginals(DiscreteFactorGraph{pruned});
     for (auto dkey : pruned.discreteKeys()) {
       const Vector probabilities = marginals.marginalProbabilities(dkey);
 
       int index = -1;
-      auto threshold = (probabilities.array() > *deadModeThreshold);
+      auto threshold = (probabilities.array() > *marginalThreshold);
       // If atleast 1 value is non-zero, then we can find the index
       // Else if all are zero, index would be set to 0 which is incorrect
       if (!threshold.isZero()) {
diff --git a/gtsam/discrete/DiscreteBayesNet.h b/gtsam/discrete/DiscreteBayesNet.h
index 01b452865..eea1739f6 100644
--- a/gtsam/discrete/DiscreteBayesNet.h
+++ b/gtsam/discrete/DiscreteBayesNet.h
@@ -128,12 +128,12 @@ class GTSAM_EXPORT DiscreteBayesNet: public BayesNet<DiscreteConditional> {
      * @brief Prune the Bayes net
      *
      * @param maxNrLeaves The maximum number of leaves to keep.
-     * @param deadModeThreshold If given, threshold on marginals to prune variables.
+     * @param marginalThreshold If given, threshold on marginals to prune variables.
      * @param fixedValues If given, return the fixed values removed.
      * @return A new DiscreteBayesNet with pruned conditionals.
      */
     DiscreteBayesNet prune(size_t maxNrLeaves,
-                           const std::optional<double>& deadModeThreshold = {},
+                           const std::optional<double>& marginalThreshold = {},
                            DiscreteValues* fixedValues = nullptr) const;
 
     ///@}
diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index ea4c3e80b..5353fe2e0 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -43,7 +43,8 @@ bool HybridBayesNet::equals(const This &bn, double tol) const {
 
 /* ************************************************************************* */
 HybridBayesNet HybridBayesNet::prune(
-    size_t maxNrLeaves, const std::optional<double> &deadModeThreshold) const {
+    size_t maxNrLeaves, const std::optional<double> &marginalThreshold,
+    DiscreteValues *fixedValues) const {
 #if GTSAM_HYBRID_TIMING
   gttic_(HybridPruning);
 #endif
@@ -52,14 +53,14 @@ HybridBayesNet HybridBayesNet::prune(
 
   // Prune discrete Bayes net
   DiscreteValues fixed;
-  auto prunedBN = marginal.prune(maxNrLeaves, deadModeThreshold, &fixed);
+  auto prunedBN = marginal.prune(maxNrLeaves, marginalThreshold, &fixed);
 
   // Multiply into one big conditional. NOTE: possibly quite expensive.
   DiscreteConditional pruned;
   for (auto &&conditional : prunedBN) pruned = pruned * (*conditional);
 
   // Set the fixed values if requested.
-  if (deadModeThreshold && fixedValues) {
+  if (marginalThreshold && fixedValues) {
     *fixedValues = fixed;
   }
 
@@ -71,7 +72,7 @@ HybridBayesNet HybridBayesNet::prune(
     if (conditional->isDiscrete()) continue;
 
     // No-op if not a HybridGaussianConditional.
-    if (deadModeThreshold) conditional = conditional->restrict(fixed);
+    if (marginalThreshold) conditional = conditional->restrict(fixed);
 
     // Now decide on type what to do:
     if (auto hgc = conditional->asHybrid()) {
diff --git a/gtsam/hybrid/HybridBayesNet.h b/gtsam/hybrid/HybridBayesNet.h
index fb05e2407..0840cb381 100644
--- a/gtsam/hybrid/HybridBayesNet.h
+++ b/gtsam/hybrid/HybridBayesNet.h
@@ -217,16 +217,18 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
    * @brief Prune the Bayes Net such that we have at most maxNrLeaves leaves.
    *
    * @param maxNrLeaves Continuous values at which to compute the error.
-   * @param deadModeThreshold The threshold to check the mode marginals against.
-   * If greater than this threshold, the mode gets assigned that value and is
-   * considered "dead" for hybrid elimination.
-   * The mode can then be removed since it only has a single possible
-   * assignment.
+   * @param marginalThreshold The threshold to check the mode marginals against.
+   * @param fixedValues The fixed values resulting from dead mode removal.
+   *
+   * @note If marginal greater than this threshold, the mode gets assigned that
+   * value and is considered "dead" for hybrid elimination. The mode can then be
+   * removed since it only has a single possible assignment.
+   
    * @return A pruned HybridBayesNet
    */
-  HybridBayesNet prune(
-      size_t maxNrLeaves,
-      const std::optional<double> &deadModeThreshold = {}) const;
+  HybridBayesNet prune(size_t maxNrLeaves,
+                       const std::optional<double> &marginalThreshold = {},
+                       DiscreteValues *fixedValues = nullptr) const;
 
   /**
    * @brief Error method using HybridValues which returns specific error for
diff --git a/gtsam/hybrid/HybridSmoother.cpp b/gtsam/hybrid/HybridSmoother.cpp
index 45320896a..594c12825 100644
--- a/gtsam/hybrid/HybridSmoother.cpp
+++ b/gtsam/hybrid/HybridSmoother.cpp
@@ -81,7 +81,7 @@ void HybridSmoother::update(const HybridGaussianFactorGraph &graph,
   if (maxNrLeaves) {
     // `pruneBayesNet` sets the leaves with 0 in discreteFactor to nullptr in
     // all the conditionals with the same keys in bayesNetFragment.
-    bayesNetFragment = bayesNetFragment.prune(*maxNrLeaves, deadModeThreshold_);
+    bayesNetFragment = bayesNetFragment.prune(*maxNrLeaves, marginalThreshold_);
   }
 
   // Add the partial bayes net to the posterior bayes net.
diff --git a/gtsam/hybrid/HybridSmoother.h b/gtsam/hybrid/HybridSmoother.h
index c3f022c62..2f7bfcebb 100644
--- a/gtsam/hybrid/HybridSmoother.h
+++ b/gtsam/hybrid/HybridSmoother.h
@@ -30,18 +30,19 @@ class GTSAM_EXPORT HybridSmoother {
   HybridGaussianFactorGraph remainingFactorGraph_;
 
   /// The threshold above which we make a decision about a mode.
-  std::optional<double> deadModeThreshold_;
+  std::optional<double> marginalThreshold_;
+  DiscreteValues fixedValues_;
 
  public:
   /**
    * @brief Constructor
    *
    * @param removeDeadModes Flag indicating whether to remove dead modes.
-   * @param deadModeThreshold The threshold above which a mode gets assigned a
+   * @param marginalThreshold The threshold above which a mode gets assigned a
    * value and is considered "dead". 0.99 is a good starting value.
    */
-  HybridSmoother(const std::optional<double> deadModeThreshold = {})
-      : deadModeThreshold_(deadModeThreshold) {}
+  HybridSmoother(const std::optional<double> marginalThreshold = {})
+      : marginalThreshold_(marginalThreshold) {}
 
   /**
    * Given new factors, perform an incremental update.