diff --git a/python/gtsam/tests/test_DiscreteSearch.py b/python/gtsam/tests/test_DiscreteSearch.py
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+++ b/python/gtsam/tests/test_DiscreteSearch.py
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+"""
+GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
+Atlanta, Georgia 30332-0415
+All Rights Reserved
+
+See LICENSE for the license information
+
+Unit tests for Discrete Search.
+Author: Frank Dellaert
+"""
+
+# pylint: disable=no-name-in-module, invalid-name
+
+import unittest
+
+from dfg_utils import generate_observation_cpt, generate_transition_cpt, make_key
+from gtsam.utils.test_case import GtsamTestCase
+
+from gtsam import (
+    DiscreteConditional,
+    DiscreteFactorGraph,
+    DiscreteSearch,
+    Ordering,
+    DefaultKeyFormatter,
+)
+
+OrderingType = Ordering.OrderingType
+
+
+class TestDiscreteSearch(GtsamTestCase):
+    """Tests for Discrete Factor Graphs."""
+
+    def test_MPE_chain(self):
+        """
+        Test for numerical underflow in EliminateMPE on long chains.
+        Adapted from the toy problem of @pcl15423
+        Ref: https://github.com/borglab/gtsam/issues/1448
+        """
+        num_states = 3
+        num_obs = 200
+        desired_state = 1
+        states = list(range(num_states))
+
+        X = {index: make_key("X", index, len(states)) for index in range(num_obs)}
+        Z = {index: make_key("Z", index, num_obs + 1) for index in range(num_obs)}
+        graph = DiscreteFactorGraph()
+
+        transition_cpt = generate_transition_cpt(num_states)
+        for i in reversed(range(1, num_obs)):
+            transition_conditional = DiscreteConditional(
+                X[i], [X[i - 1]], transition_cpt
+            )
+            graph.push_back(transition_conditional)
+
+        # Contrived example such that the desired state gives measurements [0, num_obs) with equal
+        # probability but all other states always give measurement num_obs
+        obs_cpt = generate_observation_cpt(num_states, num_obs, desired_state)
+        # Contrived example where each measurement is its own index
+        for i in range(num_obs):
+            obs_conditional = DiscreteConditional(Z[i], [X[i]], obs_cpt)
+            factor = obs_conditional.likelihood(i)
+            graph.push_back(factor)
+
+        # Check MPE
+        mpe = graph.optimize()
+        vals = [mpe[X[i][0]] for i in range(num_obs)]
+        self.assertEqual(vals, [desired_state] * num_obs)
+
+        # Create an ordering:
+        ordering = Ordering()
+        for i in reversed(range(num_obs)):
+            ordering.push_back(X[i][0])
+
+        # Now do Search
+        search = DiscreteSearch.FromFactorGraph(graph, ordering)
+        solutions = search.run(K=1)
+        mpe2 = solutions[0].assignment
+        # print({DefaultKeyFormatter(key): value for key, value in mpe2.items()})
+        vals = [mpe2[X[i][0]] for i in range(num_obs)]
+        self.assertEqual(vals, [desired_state] * num_obs)
+
+
+if __name__ == "__main__":
+    unittest.main()