diff --git a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
index d54f8a141..fb09bb618 100644
--- a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
@@ -700,8 +700,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny1Swapped) {
       m1, std::vector{conditional0, conditional1});
 
   // Add prior on m1.
-  expectedBayesNet.emplace_shared<TableDistribution>(
-      m1, "0.188638/0.811362");
+  expectedBayesNet.emplace_shared<TableDistribution>(m1, "0.188638 0.811362");
 
   // Test elimination
   const auto posterior = fg.eliminateSequential();
@@ -739,7 +738,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny2) {
   // Add prior on mode.
   // Since this is the only discrete conditional, it is added as a
   // TableDistribution.
-  expectedBayesNet.emplace_shared<TableDistribution>(mode, "23/77");
+  expectedBayesNet.emplace_shared<TableDistribution>(mode, "23 77");
 
   // Test elimination
   const auto posterior = fg.eliminateSequential();
diff --git a/gtsam/hybrid/tests/testHybridMotionModel.cpp b/gtsam/hybrid/tests/testHybridMotionModel.cpp
index 3c00d607c..5d307e81f 100644
--- a/gtsam/hybrid/tests/testHybridMotionModel.cpp
+++ b/gtsam/hybrid/tests/testHybridMotionModel.cpp
@@ -144,7 +144,7 @@ TEST(HybridGaussianFactorGraph, TwoStateModel) {
     // Since no measurement on x1, we hedge our bets
     // Importance sampling run with 100k samples gives 50.051/49.949
     // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
-    TableDistribution expected(m1, "50/50");
+    TableDistribution expected(m1, "50 50");
     EXPECT(assert_equal(expected,
                         *(bn->at(2)->asDiscrete<TableDistribution>())));
   }
@@ -162,7 +162,7 @@ TEST(HybridGaussianFactorGraph, TwoStateModel) {
     // Since we have a measurement on x1, we get a definite result
     // Values taken from an importance sampling run with 100k samples:
     // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
-    TableDistribution expected(m1, "44.3854/55.6146");
+    TableDistribution expected(m1, "44.3854 55.6146");
     EXPECT(assert_equal(
         expected, *(bn->at(2)->asDiscrete<TableDistribution>()), 0.02));
   }
@@ -251,7 +251,7 @@ TEST(HybridGaussianFactorGraph, TwoStateModel2) {
 
     // Values taken from an importance sampling run with 100k samples:
     // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
-    TableDistribution expected(m1, "48.3158/51.6842");
+    TableDistribution expected(m1, "48.3158 51.6842");
     EXPECT(assert_equal(
         expected, *(eliminated->at(2)->asDiscrete<TableDistribution>()),
         0.02));
@@ -268,7 +268,7 @@ TEST(HybridGaussianFactorGraph, TwoStateModel2) {
 
     // Values taken from an importance sampling run with 100k samples:
     // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
-    TableDistribution expected(m1, "55.396/44.604");
+    TableDistribution expected(m1, "55.396 44.604");
     EXPECT(assert_equal(
         expected, *(bn->at(2)->asDiscrete<TableDistribution>()), 0.02));
   }
@@ -346,7 +346,7 @@ TEST(HybridGaussianFactorGraph, TwoStateModel3) {
 
     // Values taken from an importance sampling run with 100k samples:
     // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
-    TableDistribution expected(m1, "51.7762/48.2238");
+    TableDistribution expected(m1, "51.7762 48.2238");
     EXPECT(assert_equal(
         expected, *(bn->at(2)->asDiscrete<TableDistribution>()), 0.02));
   }
@@ -362,7 +362,7 @@ TEST(HybridGaussianFactorGraph, TwoStateModel3) {
 
     // Values taken from an importance sampling run with 100k samples:
     // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
-    TableDistribution expected(m1, "49.0762/50.9238");
+    TableDistribution expected(m1, "49.0762 50.9238");
     EXPECT(assert_equal(
         expected, *(bn->at(2)->asDiscrete<TableDistribution>()), 0.05));
   }
@@ -389,7 +389,7 @@ TEST(HybridGaussianFactorGraph, TwoStateModel4) {
 
   // Values taken from an importance sampling run with 100k samples:
   // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
-  TableDistribution expected(m1, "8.91527/91.0847");
+  TableDistribution expected(m1, "8.91527 91.0847");
   EXPECT(assert_equal(
       expected, *(bn->at(2)->asDiscrete<TableDistribution>()), 0.01));
 }