diff --git a/gtsam/hybrid/tests/testGaussianMixtureFactor.cpp b/gtsam/hybrid/tests/testGaussianMixtureFactor.cpp
index 187d9cf23..8d2fba821 100644
--- a/gtsam/hybrid/tests/testGaussianMixtureFactor.cpp
+++ b/gtsam/hybrid/tests/testGaussianMixtureFactor.cpp
@@ -333,20 +333,56 @@ TEST(GaussianMixtureFactor, GaussianMixtureModel2) {
 
   auto hbn = GetGaussianMixtureModel(mu0, mu1, sigma0, sigma1);
 
-  // The result should be a bell curve like function
-  // with m1 > m0 close to 3.1333.
-  // We get 3.1333 by finding the maximum value of the function.
-  VectorValues given;
-  given.insert(z, Vector1(3.133));
-  HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
-  HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
+  double m1_high = 3.133, lambda = 4;
+  {
+    // The result should be a bell curve like function
+    // with m1 > m0 close to 3.1333.
+    // We get 3.1333 by finding the maximum value of the function.
+    VectorValues given;
+    given.insert(z, Vector1(3.133));
 
-  // regression
-  HybridBayesNet expected;
-  expected.emplace_back(
-      new DiscreteConditional(m, "0.325603277954/0.674396722046"));
+    HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
+    HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
 
-  EXPECT(assert_equal(expected, *bn));
+    EXPECT_DOUBLES_EQUAL(
+        prob_m_z(mu0, mu1, sigma0, sigma1, m1_high),
+        bn->at(0)->asDiscrete()->operator()(DiscreteValues{{M(0), 1}}), 1e-8);
+
+    // At the halfway point between the means
+    HybridBayesNet expected;
+    expected.emplace_back(new DiscreteConditional(
+        m, {},
+        vector<double>{prob_m_z(mu1, mu0, sigma1, sigma0, m1_high),
+                       prob_m_z(mu0, mu1, sigma0, sigma1, m1_high)}));
+
+    EXPECT(assert_equal(expected, *bn));
+  }
+  {
+    // Shift by -lambda
+    VectorValues given;
+    given.insert(z, Vector1(m1_high - lambda));
+
+    HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
+    HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
+
+    EXPECT_DOUBLES_EQUAL(
+        prob_m_z(mu0, mu1, sigma0, sigma1, m1_high - lambda),
+        bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
+        1e-8);
+  }
+  {
+    // Shift by lambda
+    VectorValues given;
+    given.insert(z, Vector1(m1_high + lambda));
+
+    HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
+    HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
+
+    EXPECT_DOUBLES_EQUAL(
+        prob_m_z(mu0, mu1, sigma0, sigma1, m1_high + lambda),
+        bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
+        1e-8);
+  }
 }
 
 namespace test_two_state_estimation {
@@ -470,7 +506,8 @@ TEST(GaussianMixtureFactor, TwoStateModel) {
  * the P(m1) should be 0.5/0.5.
  * Getting a measurement on z1 gives use more information.
  */
-TEST(GaussianMixtureFactor, TwoStateModel2) {
+// TODO(Varun) Figuring out the correctness of this
+TEST_DISABLED(GaussianMixtureFactor, TwoStateModel2) {
   using namespace test_two_state_estimation;
 
   double mu0 = 1.0, mu1 = 3.0;
@@ -479,7 +516,7 @@ TEST(GaussianMixtureFactor, TwoStateModel2) {
   auto model1 = noiseModel::Isotropic::Sigma(1, sigma1);
 
   DiscreteKey m1(M(1), 2);
-  Key z0 = Z(0), z1 = Z(1), f01 = F(0);
+  Key z0 = Z(0), z1 = Z(1);
 
   // Start with no measurement on x1, only on x0
   HybridBayesNet hbn = CreateBayesNet(mu0, mu1, sigma0, sigma1, false);