diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index 7d1834723..d3c77d83e 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -39,18 +39,18 @@ HybridBayesNet HybridBayesNet::prune(
 
   // Functional which loops over all assignments and create a set of
   // GaussianConditionals
-  auto pruner =
-      [&](const Assignment<Key> &choices,
-          const GaussianFactor::shared_ptr &gf) -> GaussianFactor::shared_ptr {
+  auto pruner = [&](const Assignment<Key> &choices,
+                    const GaussianConditional::shared_ptr &conditional)
+      -> GaussianConditional::shared_ptr {
     // typecast so we can use this to get probability value
     DiscreteValues values(choices);
 
     if ((*discreteFactor)(values) == 0.0) {
       // empty aka null pointer
-      boost::shared_ptr<GaussianFactor> null;
+      boost::shared_ptr<GaussianConditional> null;
       return null;
     } else {
-      return gf;
+      return conditional;
     }
   };
 
@@ -74,28 +74,28 @@ HybridBayesNet HybridBayesNet::prune(
         continue;
       }
 
-      // The GaussianMixture stores all the conditionals and uneliminated
-      // factors in the factors tree.
-      auto gaussianMixtureTree = gaussianMixture->factors();
-
       // Run the pruning to get a new, pruned tree
-      auto prunedFactors = gaussianMixtureTree.apply(pruner);
+      GaussianMixture::Conditionals prunedTree =
+          gaussianMixture->conditionals().apply(pruner);
 
       DiscreteKeys discreteKeys = gaussianMixture->discreteKeys();
       // reverse keys to get a natural ordering
       std::reverse(discreteKeys.begin(), discreteKeys.end());
 
-      // Convert to GaussianConditionals
-      auto prunedTree = GaussianMixture::Conditionals(
-          prunedFactors, [](const GaussianFactor::shared_ptr &factor) {
-            return boost::dynamic_pointer_cast<GaussianConditional>(factor);
-          });
+      // Convert from boost::iterator_range to std::vector.
+      std::vector<Key> frontals, parents;
+      for (Key key : gaussianMixture->frontals()) {
+        frontals.push_back(key);
+      }
+      for (Key key : gaussianMixture->parents()) {
+        parents.push_back(key);
+      }
 
       // Create the new gaussian mixture and add it to the bayes net.
       auto prunedGaussianMixture = boost::make_shared<GaussianMixture>(
-          gaussianMixture->frontals(), gaussianMixture->parents(), discreteKeys,
-          prunedTree);
+          frontals, parents, discreteKeys, prunedTree);
 
+      // Type-erase and add to the pruned Bayes Net fragment.
       prunedBayesNetFragment.push_back(
           boost::make_shared<HybridConditional>(prunedGaussianMixture));
 
diff --git a/gtsam/hybrid/HybridBayesNet.h b/gtsam/hybrid/HybridBayesNet.h
index 43eead280..834c4c3ef 100644
--- a/gtsam/hybrid/HybridBayesNet.h
+++ b/gtsam/hybrid/HybridBayesNet.h
@@ -17,6 +17,7 @@
 
 #pragma once
 
+#include <gtsam/discrete/DecisionTreeFactor.h>
 #include <gtsam/hybrid/HybridConditional.h>
 #include <gtsam/inference/BayesNet.h>
 
@@ -35,7 +36,10 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
   using sharedConditional = boost::shared_ptr<ConditionalType>;
 
   /** Construct empty bayes net */
-  HybridBayesNet() = default;
+  HybridBayesNet() : Base() {}
+
+  HybridBayesNet prune(
+      const DecisionTreeFactor::shared_ptr &discreteFactor) const;
 };
 
 }  // namespace gtsam