diff --git a/gtsam/hybrid/HybridGaussianFactor.cpp b/gtsam/hybrid/HybridGaussianFactor.cpp
index d3b26d4ef8..f1e8a8498a 100644
--- a/gtsam/hybrid/HybridGaussianFactor.cpp
+++ b/gtsam/hybrid/HybridGaussianFactor.cpp
@@ -233,7 +233,7 @@ static double PotentiallyPrunedComponentError(
 AlgebraicDecisionTree<Key> HybridGaussianFactor::errorTree(
     const VectorValues &continuousValues) const {
   // functor to convert from sharedFactor to double error value.
-  auto errorFunc = [this, &continuousValues](const auto &pair) {
+  auto errorFunc = [&continuousValues](const auto &pair) {
     return PotentiallyPrunedComponentError(pair.first, continuousValues);
   };
   DecisionTree<Key, double> error_tree(factors_, errorFunc);
diff --git a/gtsam/hybrid/HybridNonlinearFactorGraph.cpp b/gtsam/hybrid/HybridNonlinearFactorGraph.cpp
index 0e7e9c692d..56b75d15ee 100644
--- a/gtsam/hybrid/HybridNonlinearFactorGraph.cpp
+++ b/gtsam/hybrid/HybridNonlinearFactorGraph.cpp
@@ -181,19 +181,19 @@ HybridGaussianFactorGraph::shared_ptr HybridNonlinearFactorGraph::linearize(
 
 /* ************************************************************************* */
 AlgebraicDecisionTree<Key> HybridNonlinearFactorGraph::errorTree(
-    const Values& values) const {
+    const Values& continuousValues) const {
   AlgebraicDecisionTree<Key> result(0.0);
 
   // Iterate over each factor.
   for (auto& factor : factors_) {
     if (auto hnf = std::dynamic_pointer_cast<HybridNonlinearFactor>(factor)) {
       // Compute factor error and add it.
-      result = result + hnf->errorTree(values);
+      result = result + hnf->errorTree(continuousValues);
 
     } else if (auto nf = std::dynamic_pointer_cast<NonlinearFactor>(factor)) {
       // If continuous only, get the (double) error
       // and add it to every leaf of the result
-      result = result + nf->error(values);
+      result = result + nf->error(continuousValues);
 
     } else if (auto df = std::dynamic_pointer_cast<DiscreteFactor>(factor)) {
       // If discrete, just add its errorTree as well
@@ -210,4 +210,16 @@ AlgebraicDecisionTree<Key> HybridNonlinearFactorGraph::errorTree(
   return result;
 }
 
+/* ************************************************************************ */
+AlgebraicDecisionTree<Key> HybridNonlinearFactorGraph::discretePosterior(
+    const Values& continuousValues) const {
+  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 p / p.sum();
+}
+
+/* ************************************************************************ */
 }  // namespace gtsam
diff --git a/gtsam/hybrid/HybridNonlinearFactorGraph.h b/gtsam/hybrid/HybridNonlinearFactorGraph.h
index 53920a4aad..dd18cfa601 100644
--- a/gtsam/hybrid/HybridNonlinearFactorGraph.h
+++ b/gtsam/hybrid/HybridNonlinearFactorGraph.h
@@ -98,10 +98,23 @@ class GTSAM_EXPORT HybridNonlinearFactorGraph : public HybridFactorGraph {
    *
    * @note: Gaussian and hybrid Gaussian factors are not considered!
    *
-   * @param values Manifold values at which to compute the error.
+   * @param continuousValues Manifold values at which to compute the error.
    * @return AlgebraicDecisionTree<Key>
    */
-  AlgebraicDecisionTree<Key> errorTree(const Values& values) const;
+  AlgebraicDecisionTree<Key> errorTree(const Values& continuousValues) const;
+
+  /**
+   * @brief Computer posterior P(M|X=x) when all continuous values X are given.
+   * This is efficient as this simply takes -exp(.) of errorTree and normalizes.
+   *
+   * @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
+   */
+  AlgebraicDecisionTree<Key> discretePosterior(
+      const Values& continuousValues) const;
 
   /// @}
 };