diff --git a/gtsam/discrete/DiscreteBayesNet.cpp b/gtsam/discrete/DiscreteBayesNet.cpp
index f00eca60c..c1aa18828 100644
--- a/gtsam/discrete/DiscreteBayesNet.cpp
+++ b/gtsam/discrete/DiscreteBayesNet.cpp
@@ -65,10 +65,6 @@ DiscreteValues DiscreteBayesNet::sample(DiscreteValues result) const {
   return result;
 }
 
-DiscreteValues DiscreteBayesNet::mode() const {
-  return DiscreteFactorGraph(*this).optimize();
-}
-
 /* *********************************************************************** */
 std::string DiscreteBayesNet::markdown(
     const KeyFormatter& keyFormatter,
diff --git a/gtsam/discrete/DiscreteBayesNet.h b/gtsam/discrete/DiscreteBayesNet.h
index 3bcdcfe84..a5a4621aa 100644
--- a/gtsam/discrete/DiscreteBayesNet.h
+++ b/gtsam/discrete/DiscreteBayesNet.h
@@ -124,14 +124,6 @@ class GTSAM_EXPORT DiscreteBayesNet: public BayesNet<DiscreteConditional> {
      */
     DiscreteValues sample(DiscreteValues given) const;
 
-    /**
-     * @brief Compute the discrete assignment which gives the highest
-     * probability for the DiscreteBayesNet.
-     *
-     * @return DiscreteValues
-     */
-    DiscreteValues mode() const;
-
     ///@}
     /// @name Wrapper support
     /// @{
diff --git a/gtsam/discrete/discrete.i b/gtsam/discrete/discrete.i
index 43d2559d9..0f34840bf 100644
--- a/gtsam/discrete/discrete.i
+++ b/gtsam/discrete/discrete.i
@@ -193,7 +193,6 @@ class DiscreteBayesNet {
 
   gtsam::DiscreteValues sample() const;
   gtsam::DiscreteValues sample(gtsam::DiscreteValues given) const;
-  gtsam::DiscreteValues mode() const;
 
   string dot(
       const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter,
diff --git a/gtsam/discrete/tests/testDiscreteBayesNet.cpp b/gtsam/discrete/tests/testDiscreteBayesNet.cpp
index b87e1c67a..49a360cbb 100644
--- a/gtsam/discrete/tests/testDiscreteBayesNet.cpp
+++ b/gtsam/discrete/tests/testDiscreteBayesNet.cpp
@@ -120,55 +120,6 @@ TEST(DiscreteBayesNet, Asia) {
   EXPECT(assert_equal(expectedSample, actualSample));
 }
 
-/* ************************************************************************* */
-TEST(DiscreteBayesNet, Mode) {
-  DiscreteBayesNet asia;
-
-  // We need to order the Bayes net in bottom-up fashion
-  asia.add(XRay | Either = "95/5 2/98");
-  asia.add((Dyspnea | Either, Bronchitis) = "9/1 2/8 3/7 1/9");
-
-  asia.add((Either | Tuberculosis, LungCancer) = "F T T T");
-
-  asia.add(Tuberculosis | Asia = "99/1 95/5");
-  asia.add(LungCancer | Smoking = "99/1 90/10");
-  asia.add(Bronchitis | Smoking = "70/30 40/60");
-
-  asia.add(Asia, "99/1");
-  asia.add(Smoking % "50/50");  // Signature version
-
-  DiscreteValues actual = asia.mode();
-  // NOTE: Examined the DBN and found the optimal assignment.
-  DiscreteValues expected{
-      {Asia.first, 0},       {Smoking.first, 0},    {Tuberculosis.first, 0},
-      {LungCancer.first, 0}, {Bronchitis.first, 0}, {Either.first, 0},
-      {XRay.first, 0},       {Dyspnea.first, 0},
-  };
-  EXPECT(assert_equal(expected, actual));
-}
-
-/* ************************************************************************* */
-TEST(DiscreteBayesNet, ModeEdgeCase) {
-  // Declare 2 keys
-  DiscreteKey A(0, 2), B(1, 2);
-
-  // Create Bayes net such that marginal on A is bigger for 0 than 1, but the
-  // MPE does not have A=0.
-  DiscreteBayesNet bayesNet;
-  bayesNet.add(B | A = "1/1 1/2");
-  bayesNet.add(A % "10/9");
-
-  // Which we verify using max-product:
-  DiscreteFactorGraph graph(bayesNet);
-  // The expected MPE is A=1, B=1
-  DiscreteValues expectedMPE = graph.optimize();
-
-  auto actualMPE = bayesNet.mode();
-
-  EXPECT(assert_equal(expectedMPE, actualMPE));
-  EXPECT_DOUBLES_EQUAL(0.315789, bayesNet(expectedMPE), 1e-5);  // regression
-}
-
 /* ************************************************************************* */
 TEST(DiscreteBayesNet, Sugar) {
   DiscreteKey T(0, 2), L(1, 2), E(2, 2), C(8, 3), S(7, 2);