diff --git a/gtsam/hybrid/tests/TinyHybridExample.h b/gtsam/hybrid/tests/TinyHybridExample.h
index a33a45179..899a353b6 100644
--- a/gtsam/hybrid/tests/TinyHybridExample.h
+++ b/gtsam/hybrid/tests/TinyHybridExample.h
@@ -39,7 +39,7 @@ HybridBayesNet createHybridBayesNet(int num_measurements = 1) {
   // Create hybrid Bayes net.
   HybridBayesNet bayesNet;
 
-  // Create Gaussian mixture Z(0) = X(0) + noise for each measurement.
+  // Create Gaussian mixture z_i = x0 + noise for each measurement.
   for (int i = 0; i < num_measurements; i++) {
     const auto conditional0 = boost::make_shared<GaussianConditional>(
         GaussianConditional::FromMeanAndStddev(Z(i), I_1x1, X(0), Z_1x1, 0.5));
@@ -51,7 +51,7 @@ HybridBayesNet createHybridBayesNet(int num_measurements = 1) {
 
   // Create prior on X(0).
   const auto prior_on_x0 =
-      GaussianConditional::FromMeanAndStddev(X(0), Vector1(5.0), 5.0);
+      GaussianConditional::FromMeanAndStddev(X(0), Vector1(5.0), 0.5);
   bayesNet.emplaceGaussian(prior_on_x0);  // copy :-(
 
   // Add prior on mode.
@@ -61,12 +61,12 @@ HybridBayesNet createHybridBayesNet(int num_measurements = 1) {
 }
 
 HybridGaussianFactorGraph convertBayesNet(const HybridBayesNet& bayesNet,
-                                          const HybridValues& sample) {
+                                          const HybridValues& values) {
   HybridGaussianFactorGraph fg;
   int num_measurements = bayesNet.size() - 2;
   for (int i = 0; i < num_measurements; i++) {
     auto conditional = bayesNet.atMixture(i);
-    auto factor = conditional->likelihood(sample.continuousSubset({Z(i)}));
+    auto factor = conditional->likelihood(values.continuousSubset({Z(i)}));
     fg.push_back(factor);
   }
   fg.push_back(bayesNet.atGaussian(num_measurements));
@@ -75,10 +75,19 @@ HybridGaussianFactorGraph convertBayesNet(const HybridBayesNet& bayesNet,
 }
 
 HybridGaussianFactorGraph createHybridGaussianFactorGraph(
-    int num_measurements = 1) {
+    int num_measurements = 1, bool deterministic = false) {
   auto bayesNet = createHybridBayesNet(num_measurements);
-  auto sample = bayesNet.sample();
-  return convertBayesNet(bayesNet, sample);
+  if (deterministic) {
+    // Create a deterministic set of measurements:
+    HybridValues values{{}, {{M(0), 0}}};
+    for (int i = 0; i < num_measurements; i++) {
+      values.insert(Z(i), Vector1(4.0 + 1.0 * i));
+    }
+    return convertBayesNet(bayesNet, values);
+  } else {
+    // Create a random set of measurements:
+    return convertBayesNet(bayesNet, bayesNet.sample());
+  }
 }
 
 }  // namespace tiny
diff --git a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
index a104dac4b..8f50895fe 100644
--- a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
@@ -617,7 +617,10 @@ TEST(HybridGaussianFactorGraph, ErrorAndProbPrimeTree) {
 /* ****************************************************************************/
 // SumFrontals just assembles Gaussian factor graphs for each assignment.
 TEST(HybridGaussianFactorGraph, SumFrontals) {
-  auto fg = tiny::createHybridGaussianFactorGraph();
+  const int num_measurements = 1;
+  const bool deterministic = true;
+  auto fg =
+      tiny::createHybridGaussianFactorGraph(num_measurements, deterministic);
   EXPECT_LONGS_EQUAL(3, fg.size());
 
   auto sum = fg.SumFrontals();
@@ -635,15 +638,39 @@ TEST(HybridGaussianFactorGraph, SumFrontals) {
 
   // Expected decision tree with two factor graphs:
   // f(x0;mode=0)P(x0) and f(x0;mode=1)P(x0)
-  GaussianMixture::Sum expected{
+  GaussianMixture::Sum expectedSum{
       M(0),
       {GaussianFactorGraph(std::vector<GF>{mixture->factor(d0), prior}),
        mixture->constant(d0)},
       {GaussianFactorGraph(std::vector<GF>{mixture->factor(d1), prior}),
        mixture->constant(d1)}};
 
-  EXPECT(assert_equal(expected(d0), sum(d0), 1e-5));
-  EXPECT(assert_equal(expected(d1), sum(d1), 1e-5));
+  EXPECT(assert_equal(expectedSum(d0), sum(d0), 1e-5));
+  EXPECT(assert_equal(expectedSum(d1), sum(d1), 1e-5));
+
+  // Create expected Bayes Net:
+  HybridBayesNet bayesNet;
+
+  // Create Gaussian mixture on X(0).
+  using tiny::mode;
+  const auto conditional0 = boost::make_shared<GaussianConditional>(
+      X(0), Vector1(12.7279),
+      I_1x1 * 2.82843);  // regression, but mean checked to be 4.5
+  const auto conditional1 = boost::make_shared<GaussianConditional>(
+      X(0), Vector1(10.0831),
+      I_1x1 * 2.02759);  // regression, but mean 4.97297is close to prior.
+  GaussianMixture gm({X(0)}, {}, {mode}, {conditional0, conditional1});
+  bayesNet.emplaceMixture(gm);  // copy :-(
+
+  // Add prior on mode.
+  bayesNet.emplaceDiscrete(mode, "4/6");
+
+  // Test elimination
+  Ordering ordering;
+  ordering.push_back(X(0));
+  ordering.push_back(M(0));
+  const auto posterior = fg.eliminateSequential(ordering);
+  EXPECT(assert_equal(bayesNet, *posterior, 1e-4));
 }
 
 /* ************************************************************************* */