diff --git a/python/gtsam/tests/test_HybridFactorGraph.py b/python/gtsam/tests/test_HybridFactorGraph.py
index 5398160dc..700137d21 100644
--- a/python/gtsam/tests/test_HybridFactorGraph.py
+++ b/python/gtsam/tests/test_HybridFactorGraph.py
@@ -119,7 +119,15 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         return bayesNet
 
     @staticmethod
-    def factor_graph_from_bayes_net(bayesNet: HybridBayesNet, sample: HybridValues):
+    def measurements(sample: HybridValues, indices) -> gtsam.VectorValues:
+        """Create measurements from a sample, grabbing Z(i) where i in indices."""
+        measurements = gtsam.VectorValues()
+        for i in indices:
+            measurements.insert(Z(i), sample.at(Z(i)))
+        return measurements
+
+    @classmethod
+    def factor_graph_from_bayes_net(cls, bayesNet: HybridBayesNet, sample: HybridValues):
         """Create a factor graph from the Bayes net with sampled measurements.
             The factor graph is `P(x)P(n) ϕ(x, n; z0) ϕ(x, n; z1) ...`
             and thus represents the same joint probability as the Bayes net.
@@ -128,9 +136,7 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         num_measurements = bayesNet.size() - 2
         for i in range(num_measurements):
             conditional = bayesNet.atMixture(i)
-            measurement = gtsam.VectorValues()
-            measurement.insert(Z(i), sample.at(Z(i)))
-            factor = conditional.likelihood(measurement)
+            factor = conditional.likelihood(cls.measurements(sample, [i]))
             fg.push_back(factor)
         fg.push_back(bayesNet.atGaussian(num_measurements))
         fg.push_back(bayesNet.atDiscrete(num_measurements+1))
@@ -147,11 +153,10 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
 
         # Do importance sampling.
         num_measurements = bayesNet.size() - 2
+        measurements = cls.measurements(sample, range(num_measurements))
         for s in range(N):
             proposed = prior.sample()
-            for i in range(num_measurements):
-                z_i = sample.at(Z(i))
-                proposed.insert(Z(i), z_i)
+            proposed.insert(measurements)
             weight = bayesNet.evaluate(proposed) / prior.evaluate(proposed)
             marginals[proposed.atDiscrete(M(0))] += weight
 
@@ -213,15 +218,13 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         fg = self.factor_graph_from_bayes_net(bayesNet, sample)
         self.assertEqual(fg.size(), 4)
 
+        # Create measurements from the sample.
+        measurements = self.measurements(sample, [0, 1])
+
         # Calculate ratio between Bayes net probability and the factor graph:
         expected_ratio = self.calculate_ratio(bayesNet, fg, sample)
         # print(f"expected_ratio: {expected_ratio}\n")
 
-        # Create measurements from the sample.
-        measurements = gtsam.VectorValues()
-        for i in range(2):
-            measurements.insert(Z(i), sample.at(Z(i)))
-
         # Check with a number of other samples.
         for i in range(10):
             other = bayesNet.sample()
@@ -231,6 +234,25 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
             if (ratio > 0):
                 self.assertAlmostEqual(ratio, expected_ratio)
 
+        # Test elimination.
+        ordering = gtsam.Ordering()
+        ordering.push_back(X(0))
+        ordering.push_back(M(0))
+        posterior = fg.eliminateSequential(ordering)
+        print(posterior)
+
+        # Calculate ratio between Bayes net probability and the factor graph:
+        expected_ratio = self.calculate_ratio(posterior, fg, sample)
+        print(f"expected_ratio: {expected_ratio}\n")
+
+        # Check with a number of other samples.
+        for i in range(10):
+            other = posterior.sample()
+            other.insert(measurements)
+            ratio = self.calculate_ratio(posterior, fg, other)
+            print(f"Ratio: {ratio}\n")
+            # if (ratio > 0):
+            #     self.assertAlmostEqual(ratio, expected_ratio)
 
 if __name__ == "__main__":
     unittest.main()