diff --git a/gtsam/hybrid/GaussianMixture.cpp b/gtsam/hybrid/GaussianMixture.cpp
index 435410358..52f1a3f0a 100644
--- a/gtsam/hybrid/GaussianMixture.cpp
+++ b/gtsam/hybrid/GaussianMixture.cpp
@@ -174,18 +174,6 @@ KeyVector GaussianMixture::continuousParents() const {
   return continuousParentKeys;
 }
 
-/* ************************************************************************* */
-boost::shared_ptr<DecisionTreeFactor> GaussianMixture::normalizationConstants()
-    const {
-  DecisionTree<Key, double> constants(
-      conditionals_, [&](const GaussianConditional::shared_ptr &conditional) {
-        return conditional->normalizationConstant();
-      });
-  // If all constants the same, return nullptr:
-  if (constants.nrLeaves() == 1) return nullptr;
-  return boost::make_shared<DecisionTreeFactor>(discreteKeys(), constants);
-}
-
 /* ************************************************************************* */
 bool GaussianMixture::allFrontalsGiven(const VectorValues &given) const {
   for (auto &&kv : given) {
diff --git a/gtsam/hybrid/GaussianMixture.h b/gtsam/hybrid/GaussianMixture.h
index 64eda218e..eef0419a2 100644
--- a/gtsam/hybrid/GaussianMixture.h
+++ b/gtsam/hybrid/GaussianMixture.h
@@ -160,10 +160,6 @@ class GTSAM_EXPORT GaussianMixture
   /// log-normalization constants.
   double logNormalizationConstant() const override { return logConstant_; }
 
-  /// Return a discrete factor with possibly varying normalization constants.
-  /// If there is no variation, return nullptr.
-  boost::shared_ptr<DecisionTreeFactor> normalizationConstants() const;
-
   /**
    * Create a likelihood factor for a Gaussian mixture, return a Mixture factor
    * on the parents.
diff --git a/gtsam/hybrid/tests/testGaussianMixture.cpp b/gtsam/hybrid/tests/testGaussianMixture.cpp
index edc585704..7a4439e21 100644
--- a/gtsam/hybrid/tests/testGaussianMixture.cpp
+++ b/gtsam/hybrid/tests/testGaussianMixture.cpp
@@ -61,9 +61,6 @@ const GaussianMixture mixture({Z(0)}, {X(0)}, {mode}, conditionals);
 TEST(GaussianMixture, Invariants) {
   using namespace equal_constants;
 
-  // Check that normalizationConstants returns nullptr, as all constants equal.
-  CHECK(!mixture.normalizationConstants());
-
   // Check that the mixture normalization constant is the max of all constants
   // which are all equal, in this case, hence:
   const double K = mixture.logNormalizationConstant();
@@ -170,19 +167,6 @@ TEST(GaussianMixture, ContinuousParents) {
   EXPECT(continuousParentKeys[0] == X(0));
 }
 
-/* ************************************************************************* */
-/// Check we can create a DecisionTreeFactor with all normalization constants.
-TEST(GaussianMixture, NormalizationConstants) {
-  using namespace mode_dependent_constants;
-
-  const auto factor = mixture.normalizationConstants();
-
-  // Test with 1D Gaussian normalization constants for sigma 0.5 and 3:
-  auto c = [](double sigma) { return 1.0 / (sqrt(2 * M_PI) * sigma); };
-  const DecisionTreeFactor expected({M(0), 2}, {c(0.5), c(3)});
-  EXPECT(assert_equal(expected, *factor));
-}
-
 /* ************************************************************************* */
 /// Check that the likelihood is proportional to the conditional density given
 /// the measurements.