diff --git a/gtsam/hybrid/GaussianMixture.cpp b/gtsam/hybrid/GaussianMixture.cpp
index 24d166095..e686241fc 100644
--- a/gtsam/hybrid/GaussianMixture.cpp
+++ b/gtsam/hybrid/GaussianMixture.cpp
@@ -204,8 +204,7 @@ boost::shared_ptr<GaussianMixtureFactor> GaussianMixture::likelihood(
   const GaussianMixtureFactor::Factors likelihoods(
       conditionals_, [&](const GaussianConditional::shared_ptr &conditional) {
         return GaussianMixtureFactor::FactorAndConstant{
-            conditional->likelihood(given),
-            conditional->logNormalizationConstant()};
+            conditional->likelihood(given), 0.0};
       });
   return boost::make_shared<GaussianMixtureFactor>(
       continuousParentKeys, discreteParentKeys, likelihoods);
diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index be9cdba85..fd1d24722 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -341,11 +341,13 @@ HybridGaussianFactorGraph HybridBayesNet::toFactorGraph(
   // replace it by a likelihood factor:
   for (auto &&conditional : *this) {
     if (conditional->frontalsIn(measurements)) {
-      if (auto gc = conditional->asGaussian())
+      if (auto gc = conditional->asGaussian()) {
         fg.push_back(gc->likelihood(measurements));
-      else if (auto gm = conditional->asMixture())
+      } else if (auto gm = conditional->asMixture()) {
         fg.push_back(gm->likelihood(measurements));
-      else {
+        const auto constantsFactor = gm->normalizationConstants();
+        if (constantsFactor) fg.push_back(constantsFactor);
+      } else {
         throw std::runtime_error("Unknown conditional type");
       }
     } else {
diff --git a/gtsam/hybrid/tests/testGaussianMixture.cpp b/gtsam/hybrid/tests/testGaussianMixture.cpp
index 5bad40728..024aafbc7 100644
--- a/gtsam/hybrid/tests/testGaussianMixture.cpp
+++ b/gtsam/hybrid/tests/testGaussianMixture.cpp
@@ -198,8 +198,7 @@ TEST(GaussianMixture, Likelihood) {
       gm.conditionals(),
       [measurements](const GaussianConditional::shared_ptr& conditional) {
         return GaussianMixtureFactor::FactorAndConstant{
-            conditional->likelihood(measurements),
-            conditional->logNormalizationConstant()};
+            conditional->likelihood(measurements), 0.0};
       });
   const GaussianMixtureFactor expected({X(0)}, {mode}, factors);
   EXPECT(assert_equal(expected, *factor));
diff --git a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
index f904ee5ba..ef89c0bfd 100644
--- a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
@@ -613,7 +613,7 @@ TEST(HybridGaussianFactorGraph, assembleGraphTree) {
   const int num_measurements = 1;
   auto fg = tiny::createHybridGaussianFactorGraph(
       num_measurements, VectorValues{{Z(0), Vector1(5.0)}});
-  EXPECT_LONGS_EQUAL(3, fg.size());
+  EXPECT_LONGS_EQUAL(4, fg.size());
 
   // Assemble graph tree:
   auto actual = fg.assembleGraphTree();
@@ -625,7 +625,7 @@ TEST(HybridGaussianFactorGraph, assembleGraphTree) {
   CHECK(mixture);
 
   // Get prior factor:
-  const auto gf = boost::dynamic_pointer_cast<HybridConditional>(fg.at(1));
+  const auto gf = boost::dynamic_pointer_cast<HybridConditional>(fg.at(2));
   CHECK(gf);
   using GF = GaussianFactor::shared_ptr;
   const GF prior = gf->asGaussian();
@@ -654,7 +654,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny1) {
   const int num_measurements = 1;
   auto fg = tiny::createHybridGaussianFactorGraph(
       num_measurements, VectorValues{{Z(0), Vector1(5.0)}});
-  EXPECT_LONGS_EQUAL(3, fg.size());
+  EXPECT_LONGS_EQUAL(4, fg.size());
 
   // Create expected Bayes Net:
   HybridBayesNet expectedBayesNet;
@@ -686,7 +686,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny2) {
   auto fg = tiny::createHybridGaussianFactorGraph(
       num_measurements,
       VectorValues{{Z(0), Vector1(4.0)}, {Z(1), Vector1(6.0)}});
-  EXPECT_LONGS_EQUAL(4, fg.size());
+  EXPECT_LONGS_EQUAL(6, fg.size());
 
   // Create expected Bayes Net:
   HybridBayesNet expectedBayesNet;
@@ -721,7 +721,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny22) {
   auto bn = tiny::createHybridBayesNet(num_measurements, manyModes);
   const VectorValues measurements{{Z(0), Vector1(4.0)}, {Z(1), Vector1(6.0)}};
   auto fg = bn.toFactorGraph(measurements);
-  EXPECT_LONGS_EQUAL(5, fg.size());
+  EXPECT_LONGS_EQUAL(7, fg.size());
 
   // Test elimination
   const auto posterior = fg.eliminateSequential();