diff --git a/python/gtsam/tests/test_HybridFactorGraph.py b/python/gtsam/tests/test_HybridFactorGraph.py
index d638344bb..499afe09f 100644
--- a/python/gtsam/tests/test_HybridFactorGraph.py
+++ b/python/gtsam/tests/test_HybridFactorGraph.py
@@ -22,6 +22,8 @@ from gtsam import (DiscreteConditional, DiscreteKeys, GaussianConditional,
                    HybridGaussianFactorGraph, HybridValues, JacobianFactor,
                    Ordering, noiseModel)
 
+DEBUG_MARGINALS = False
+
 
 class TestHybridGaussianFactorGraph(GtsamTestCase):
     """Unit tests for HybridGaussianFactorGraph."""
@@ -201,9 +203,10 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         # Estimate marginals using importance sampling.
         marginals = self.estimate_marginals(target=unnormalized_posterior,
                                             proposal_density=proposal_density)
-        print(f"True mode: {values.atDiscrete(M(0))}")
-        print(f"P(mode=0; Z) = {marginals[0]}")
-        print(f"P(mode=1; Z) = {marginals[1]}")
+        if DEBUG_MARGINALS:
+            print(f"True mode: {values.atDiscrete(M(0))}")
+            print(f"P(mode=0; Z) = {marginals[0]}")
+            print(f"P(mode=1; Z) = {marginals[1]}")
 
         # Check that the estimate is close to the true value.
         self.assertAlmostEqual(marginals[0], 0.74, delta=0.01)
@@ -232,9 +235,10 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         # Estimate marginals using importance sampling.
         marginals = self.estimate_marginals(target=true_posterior,
                                             proposal_density=proposal_density)
-        print(f"True mode: {values.atDiscrete(M(0))}")
-        print(f"P(mode=0; z0) = {marginals[0]}")
-        print(f"P(mode=1; z0) = {marginals[1]}")
+        if DEBUG_MARGINALS:
+            print(f"True mode: {values.atDiscrete(M(0))}")
+            print(f"P(mode=0; z0) = {marginals[0]}")
+            print(f"P(mode=1; z0) = {marginals[1]}")
 
         # Check that the estimate is close to the true value.
         self.assertAlmostEqual(marginals[0], 0.74, delta=0.01)
@@ -247,7 +251,6 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         return bayesNet.evaluate(sample) / fg.probPrime(sample) if \
             fg.probPrime(sample) > 0 else 0
 
-    @unittest.skip
     def test_ratio(self):
         """
         Given a tiny two variable hybrid model, with 2 measurements, test the
@@ -283,9 +286,10 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         # Estimate marginals using importance sampling.
         marginals = self.estimate_marginals(target=unnormalized_posterior,
                                             proposal_density=proposal_density)
-        print(f"True mode: {values.atDiscrete(M(0))}")
-        print(f"P(mode=0; Z) = {marginals[0]}")
-        print(f"P(mode=1; Z) = {marginals[1]}")
+        if DEBUG_MARGINALS:
+            print(f"True mode: {values.atDiscrete(M(0))}")
+            print(f"P(mode=0; Z) = {marginals[0]}")
+            print(f"P(mode=1; Z) = {marginals[1]}")
 
         # Check that the estimate is close to the true value.
         self.assertAlmostEqual(marginals[0], 0.23, delta=0.01)