diff --git a/gtsam/hybrid/HybridGaussianFactorGraph.cpp b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
index 8e6e95c17..0e5a34359 100644
--- a/gtsam/hybrid/HybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
@@ -505,22 +505,22 @@ EliminateHybrid(const HybridGaussianFactorGraph &factors,
 /* ************************************************************************ */
 AlgebraicDecisionTree<Key> HybridGaussianFactorGraph::errorTree(
     const VectorValues &continuousValues) const {
-  AlgebraicDecisionTree<Key> error_tree(0.0);
+  AlgebraicDecisionTree<Key> result(0.0);
   // Iterate over each factor.
   for (auto &factor : factors_) {
     if (auto f = std::dynamic_pointer_cast<HybridFactor>(factor)) {
       // Check for HybridFactor, and call errorTree
-      error_tree = error_tree + f->errorTree(continuousValues);
+      result = result + f->errorTree(continuousValues);
     } else if (auto f = std::dynamic_pointer_cast<DiscreteFactor>(factor)) {
       // Skip discrete factors
       continue;
     } else {
       // Everything else is a continuous only factor
       HybridValues hv(continuousValues, DiscreteValues());
-      error_tree = error_tree + AlgebraicDecisionTree<Key>(factor->error(hv));
+      result = result + AlgebraicDecisionTree<Key>(factor->error(hv));
     }
   }
-  return error_tree;
+  return result;
 }
 
 /* ************************************************************************ */
@@ -531,21 +531,14 @@ double HybridGaussianFactorGraph::probPrime(const HybridValues &values) const {
 }
 
 /* ************************************************************************ */
-DecisionTreeFactor HybridGaussianFactorGraph::probPrime(
+AlgebraicDecisionTree<Key> HybridGaussianFactorGraph::discretePosterior(
     const VectorValues &continuousValues) const {
-  AlgebraicDecisionTree<Key> error_tree = this->errorTree(continuousValues);
-  AlgebraicDecisionTree<Key> prob_tree = error_tree.apply([](double error) {
+  AlgebraicDecisionTree<Key> errors = this->errorTree(continuousValues);
+  AlgebraicDecisionTree<Key> p = errors.apply([](double error) {
     // NOTE: The 0.5 term is handled by each factor
     return exp(-error);
   });
-  return {GetDiscreteKeys(*this), prob_tree};
-}
-
-/* ************************************************************************ */
-DiscreteConditional HybridGaussianFactorGraph::discretePosterior(
-    const VectorValues &continuousValues) const {
-  auto p = probPrime(continuousValues);
-  return {p.size(), p};
+  return p / p.sum();
 }
 
 /* ************************************************************************ */
diff --git a/gtsam/hybrid/HybridGaussianFactorGraph.h b/gtsam/hybrid/HybridGaussianFactorGraph.h
index 5d19b4f83..3ef6218be 100644
--- a/gtsam/hybrid/HybridGaussianFactorGraph.h
+++ b/gtsam/hybrid/HybridGaussianFactorGraph.h
@@ -196,24 +196,17 @@ class GTSAM_EXPORT HybridGaussianFactorGraph
    */
   double probPrime(const HybridValues& values) const;
 
-  /**
-   * @brief Compute unnormalized probability \f$ P(X | M, Z) \f$
-   * for each discrete assignment, and return as a tree.
-   *
-   * @param continuousValues Continuous values at which to compute probability.
-   * @return DecisionTreeFactor
-   */
-  DecisionTreeFactor probPrime(const VectorValues& continuousValues) const;
-
   /**
    * @brief Computer posterior P(M|X=x) when all continuous values X are given.
-   * This is very efficient as this simply probPrime normalized into a
-   * conditional.
+   * This is efficient as this simply probPrime normalized.
+   *
+   * @note Not a DiscreteConditional as the cardinalities of the DiscreteKeys,
+   * which we would need, are hard to recover.
    *
    * @param continuousValues Continuous values x to condition on.
    * @return DecisionTreeFactor
    */
-  DiscreteConditional discretePosterior(
+  AlgebraicDecisionTree<Key> discretePosterior(
       const VectorValues& continuousValues) const;
 
   /**
diff --git a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
index 8ba1eb762..0c5f52e61 100644
--- a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
@@ -619,16 +619,9 @@ TEST(HybridGaussianFactorGraph, ErrorAndProbPrimeTree) {
   const auto error_tree = graph.errorTree(delta.continuous());
   EXPECT(assert_equal(expectedErrors, error_tree, 1e-7));
 
-  // regression test for probPrime
-  const DecisionTreeFactor expectedFactor(
-      s.modes, std::vector{0.36793249, 0.61247742, 0.59489556, 0.99029064});
-  auto probabilities = graph.probPrime(delta.continuous());
-  EXPECT(assert_equal(expectedFactor, probabilities, 1e-7));
-
   // regression test for discretePosterior
-  const DecisionTreeFactor normalized(
+  const AlgebraicDecisionTree<Key> expectedPosterior(
       s.modes, std::vector{0.14341014, 0.23872714, 0.23187421, 0.38598852});
-  DiscreteConditional expectedPosterior(2, normalized);
   auto posterior = graph.discretePosterior(delta.continuous());
   EXPECT(assert_equal(expectedPosterior, posterior, 1e-7));
 }