diff --git a/python/gtsam/tests/test_HybridFactorGraph.py b/python/gtsam/tests/test_HybridFactorGraph.py
index 481617db1..2d3513f12 100644
--- a/python/gtsam/tests/test_HybridFactorGraph.py
+++ b/python/gtsam/tests/test_HybridFactorGraph.py
@@ -6,7 +6,7 @@ All Rights Reserved
 See LICENSE for the license information
 
 Unit tests for Hybrid Factor Graphs.
-Author: Fan Jiang
+Author: Fan Jiang, Varun Agrawal, Frank Dellaert
 """
 # pylint: disable=invalid-name, no-name-in-module, no-member
 
@@ -25,6 +25,7 @@ from gtsam import (DiscreteConditional, DiscreteKeys, GaussianConditional,
 
 class TestHybridGaussianFactorGraph(GtsamTestCase):
     """Unit tests for HybridGaussianFactorGraph."""
+
     def test_create(self):
         """Test construction of hybrid factor graph."""
         model = noiseModel.Unit.Create(3)
@@ -117,23 +118,23 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
 
         return bayesNet
 
-    def test_tiny(self):
-        """Test a tiny two variable hybrid model."""
-        bayesNet = self.tiny()
-        sample = bayesNet.sample()
-        # print(sample)
-
-        # Create a factor graph from the Bayes net with sampled measurements.
+    @staticmethod
+    def factor_graph_from_bayes_net(bayesNet: gtsam.HybridBayesNet, sample: gtsam.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.
+        """
         fg = HybridGaussianFactorGraph()
-        conditional = bayesNet.atMixture(0)
-        measurement = gtsam.VectorValues()
-        measurement.insert(Z(0), sample.at(Z(0)))
-        factor = conditional.likelihood(measurement)
-        fg.push_back(factor)
-        fg.push_back(bayesNet.atGaussian(1))
-        fg.push_back(bayesNet.atDiscrete(2))
-
-        self.assertEqual(fg.size(), 3)
+        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)
+            fg.push_back(factor)
+        fg.push_back(bayesNet.atGaussian(num_measurements))
+        fg.push_back(bayesNet.atDiscrete(num_measurements+1))
+        return fg
 
     @staticmethod
     def calculate_ratio(bayesNet, fg, sample):
@@ -143,6 +144,26 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         return bayesNet.evaluate(sample) / fg.probPrime(
             continuous, sample.discrete())
 
+    def test_tiny(self):
+        """Test a tiny two variable hybrid model."""
+        bayesNet = self.tiny()
+        sample = bayesNet.sample()
+        # print(sample)
+
+        # TODO(dellaert): do importance sampling to get an estimate P(mode)
+        prior = self.tiny(num_measurements=0) # just P(x0)P(mode)
+        for s in range(100):
+            proposed = prior.sample()
+            print(proposed)
+            for i in range(2):
+                proposed.insert(Z(i), sample.at(Z(i)))
+            print(proposed)
+            weight = bayesNet.evaluate(proposed) / prior.evaluate(proposed)
+            print(weight)
+
+        fg = self.factor_graph_from_bayes_net(bayesNet, sample)
+        self.assertEqual(fg.size(), 3)
+
     def test_ratio(self):
         """
         Given a tiny two variable hybrid model, with 2 measurements,
@@ -156,20 +177,7 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         sample: gtsam.HybridValues = bayesNet.sample()
         # print(sample)
 
-        # Create a factor graph from the Bayes net with sampled measurements.
-        # The factor graph is `P(x)P(n) ϕ(x, n; z1) ϕ(x, n; z2)`
-        # and thus represents the same joint probability as the Bayes net.
-        fg = HybridGaussianFactorGraph()
-        for i in range(2):
-            conditional = bayesNet.atMixture(i)
-            measurement = gtsam.VectorValues()
-            measurement.insert(Z(i), sample.at(Z(i)))
-            factor = conditional.likelihood(measurement)
-            fg.push_back(factor)
-        fg.push_back(bayesNet.atGaussian(2))
-        fg.push_back(bayesNet.atDiscrete(3))
-
-        # print(fg)
+        fg = self.factor_graph_from_bayes_net(bayesNet, sample)
         self.assertEqual(fg.size(), 4)
 
         # Calculate ratio between Bayes net probability and the factor graph:
@@ -186,9 +194,9 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
             other = bayesNet.sample()
             other.update(measurements)
             # print(other)
-            # ratio = self.calculate_ratio(bayesNet, fg, other)
+            ratio = self.calculate_ratio(bayesNet, fg, other)
             # print(f"Ratio: {ratio}\n")
-            # self.assertAlmostEqual(ratio, expected_ratio)
+            self.assertAlmostEqual(ratio, expected_ratio)
 
 
 if __name__ == "__main__":