diff --git a/python/gtsam/tests/test_HybridBayesNet.py b/python/gtsam/tests/test_HybridBayesNet.py
new file mode 100644
index 000000000..cfe080dcb
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+++ b/python/gtsam/tests/test_HybridBayesNet.py
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+"""
+GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,
+Atlanta, Georgia 30332-0415
+All Rights Reserved
+
+See LICENSE for the license information
+
+Unit tests for Hybrid Values.
+Author: Frank Dellaert
+"""
+# pylint: disable=invalid-name, no-name-in-module, no-member
+
+from __future__ import print_function
+
+import unittest
+
+import numpy as np
+from gtsam.symbol_shorthand import A, X
+from gtsam.utils.test_case import GtsamTestCase
+
+import gtsam
+from gtsam import GaussianConditional, GaussianMixture, HybridBayesNet, HybridValues, noiseModel
+
+
+class TestHybridBayesNet(GtsamTestCase):
+    """Unit tests for HybridValues."""
+
+    def test_evaluate(self):
+        """Test evaluate for a hybrid Bayes net P(X0|X1) P(X1|Asia) P(Asia)."""
+        asiaKey = A(0)
+        Asia = (asiaKey, 2)
+
+        # Create the continuous conditional
+        I_1x1 = np.eye(1)
+        gc = GaussianConditional.FromMeanAndStddev(X(0), 2 * I_1x1, X(1), [-4], 5.0)
+
+        # Create the noise models
+        model0 = noiseModel.Diagonal.Sigmas([2.0])
+        model1 = noiseModel.Diagonal.Sigmas([3.0])
+
+        # Create the conditionals
+        conditional0 = GaussianConditional(X(1), [5], I_1x1, model0)
+        conditional1 = GaussianConditional(X(1), [2], I_1x1, model1)
+        # gm = GaussianMixture.FromConditionals([X(1)], [], [Asia], [conditional0, conditional1])  #
+
+        # Create hybrid Bayes net.
+        bayesNet = HybridBayesNet()
+        bayesNet.addGaussian(gc)
+        # bayesNet.addMixture(gm)
+        bayesNet.addDiscrete(Asia, "99/1")
+
+        # Create values at which to evaluate.
+        values = HybridValues()
+        values.insert(asiaKey, 0)
+        values.insert(X(0), [-6])
+        values.insert(X(1), [1])
+
+        conditionalProbability = gc.evaluate(values.continuous())
+        mixtureProbability = conditional0.evaluate(values.continuous())
+        assert self.assertAlmostEqual(
+            conditionalProbability * mixtureProbability * 0.99, bayesNet.evaluate(values), places=5
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()