diff --git a/gtsam/discrete/DiscreteBayesNet.cpp b/gtsam/discrete/DiscreteBayesNet.cpp
index 12c607223..7c6da3dac 100644
--- a/gtsam/discrete/DiscreteBayesNet.cpp
+++ b/gtsam/discrete/DiscreteBayesNet.cpp
@@ -71,16 +71,14 @@ DiscreteValues DiscreteBayesNet::sample(DiscreteValues result) const {
 
 /* ************************************************************************* */
 // The implementation is: build the entire joint into one factor and then prune.
-// TODO(Frank): This can be quite expensive *unless* the factors have already
+// NOTE: This can be quite expensive *unless* the factors have already
 // 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>& marginalThreshold,
     DiscreteValues* fixedValues) const {
   // Multiply into one big conditional. NOTE: possibly quite expensive.
-  DiscreteConditional joint;
-  for (const DiscreteConditional::shared_ptr& conditional : *this)
-    joint = joint * (*conditional);
+  DiscreteConditional joint = this->joint();
 
   // Prune the joint. NOTE: imperative and, again, possibly quite expensive.
   DiscreteConditional pruned = joint;
@@ -122,6 +120,15 @@ DiscreteBayesNet DiscreteBayesNet::prune(
   return result;
 }
 
+/* *********************************************************************** */
+DiscreteConditional DiscreteBayesNet::joint() const {
+  DiscreteConditional joint;
+  for (const DiscreteConditional::shared_ptr& conditional : *this)
+    joint = joint * (*conditional);
+
+  return joint;
+}
+
 /* *********************************************************************** */
 std::string DiscreteBayesNet::markdown(
     const KeyFormatter& keyFormatter,
diff --git a/gtsam/discrete/DiscreteBayesNet.h b/gtsam/discrete/DiscreteBayesNet.h
index eea1739f6..e15576b37 100644
--- a/gtsam/discrete/DiscreteBayesNet.h
+++ b/gtsam/discrete/DiscreteBayesNet.h
@@ -136,6 +136,16 @@ class GTSAM_EXPORT DiscreteBayesNet: public BayesNet<DiscreteConditional> {
                            const std::optional<double>& marginalThreshold = {},
                            DiscreteValues* fixedValues = nullptr) const;
 
+    /**
+     * @brief Multiply all conditionals into one big joint conditional
+     * and return it.
+     *
+     * NOTE: possibly quite expensive.
+     *
+     * @return DiscreteConditional
+     */
+    DiscreteConditional joint() const;
+
     ///@}
     /// @name Wrapper support
     /// @{