diff --git a/gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp b/gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp
index 6e66e5fcb..c732ec6c5 100644
--- a/gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp
+++ b/gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp
@@ -476,9 +476,9 @@ TEST(HybridFactorGraph, Full_Elimination) {
           ->equals(*discreteBayesNet.at(1)));
 }
 
-/*
-****************************************************************************/
-// Test printing
+/****************************************************************************
+ * Test printing
+ */
 TEST(HybridFactorGraph, Printing) {
   Switching self(3);
 
@@ -663,80 +663,81 @@ factor 2: Hybrid  P( x3 | m1 m2)
   EXPECT(assert_print_equal(expected_hybridBayesNet, *hybridBayesNet));
 }
 
-// /* *************************************************************************
-// */
-// // Simple PlanarSLAM example test with 2 poses and 2 landmarks (each pose
-// // connects to 1 landmark) to expose issue with default decision tree
-// creation
-// // in hybrid elimination. The hybrid factor is between the poses X0 and X1.
-// The
-// // issue arises if we eliminate a landmark variable first since it is not
-// // connected to a DCFactor.
-// TEST(HybridFactorGraph, DefaultDecisionTree) {
-//   HybridNonlinearFactorGraph fg;
+/****************************************************************************
+ * Simple PlanarSLAM example test with 2 poses and 2 landmarks (each pose
+ * connects to 1 landmark) to expose issue with default decision tree creation
+ * in hybrid elimination. The hybrid factor is between the poses X0 and X1.
+ * The issue arises if we eliminate a landmark variable first since it is not
+ * connected to a HybridFactor.
+ */
+TEST(HybridFactorGraph, DefaultDecisionTree) {
+  HybridNonlinearFactorGraph fg;
 
-//   // Add a prior on pose x1 at the origin. A prior factor consists of a mean
-//   and
-//   // a noise model (covariance matrix)
-//   Pose2 prior(0.0, 0.0, 0.0);  // prior mean is at origin
-//   auto priorNoise = noiseModel::Diagonal::Sigmas(
-//       Vector3(0.3, 0.3, 0.1));  // 30cm std on x,y, 0.1 rad on theta
-//   fg.emplace_nonlinear<PriorFactor<Pose2>>(X(0), prior, priorNoise);
+  // Add a prior on pose x1 at the origin.
+  // A prior factor consists of a mean and a noise model (covariance matrix)
+  Pose2 prior(0.0, 0.0, 0.0);  // prior mean is at origin
+  auto priorNoise = noiseModel::Diagonal::Sigmas(
+      Vector3(0.3, 0.3, 0.1));  // 30cm std on x,y, 0.1 rad on theta
+  fg.emplace_nonlinear<PriorFactor<Pose2>>(X(0), prior, priorNoise);
 
-//   using PlanarMotionModel = BetweenFactor<Pose2>;
+  using PlanarMotionModel = BetweenFactor<Pose2>;
 
-//   // Add odometry factor
-//   Pose2 odometry(2.0, 0.0, 0.0);
-//   KeyVector contKeys = {X(0), X(1)};
-//   auto noise_model = noiseModel::Isotropic::Sigma(3, 1.0);
-//   auto still = boost::make_shared<PlanarMotionModel>(X(0), X(1), Pose2(0, 0,
-//   0),
-//                                                      noise_model),
-//        moving = boost::make_shared<PlanarMotionModel>(X(0), X(1), odometry,
-//                                                       noise_model);
-//   std::vector<PlanarMotionModel::shared_ptr> components = {still, moving};
-//   auto dcFactor = boost::make_shared<DCMixtureFactor<PlanarMotionModel>>(
-//       contKeys, DiscreteKeys{gtsam::DiscreteKey(M(1), 2)}, components);
-//   fg.push_back(dcFactor);
+  // Add odometry factor
+  Pose2 odometry(2.0, 0.0, 0.0);
+  KeyVector contKeys = {X(0), X(1)};
+  auto noise_model = noiseModel::Isotropic::Sigma(3, 1.0);
+  auto still = boost::make_shared<PlanarMotionModel>(X(0), X(1), Pose2(0, 0,
+  0),
+                                                     noise_model),
+       moving = boost::make_shared<PlanarMotionModel>(X(0), X(1), odometry,
+                                                      noise_model);
+  std::vector<PlanarMotionModel::shared_ptr> motion_models = {still, moving};
+  //TODO(Varun) Make a templated constructor for MixtureFactor which does this?
+  std::vector<NonlinearFactor::shared_ptr> components;
+  for (auto&& f : motion_models) {
+    components.push_back(boost::dynamic_pointer_cast<NonlinearFactor>(f));
+  }
+  fg.emplace_hybrid<MixtureFactor>(
+      contKeys, DiscreteKeys{gtsam::DiscreteKey(M(1), 2)}, components);
 
-//   // Add Range-Bearing measurements to from X0 to L0 and X1 to L1.
-//   // create a noise model for the landmark measurements
-//   auto measurementNoise = noiseModel::Diagonal::Sigmas(
-//       Vector2(0.1, 0.2));  // 0.1 rad std on bearing, 20cm on range
-//   // create the measurement values - indices are (pose id, landmark id)
-//   Rot2 bearing11 = Rot2::fromDegrees(45), bearing22 = Rot2::fromDegrees(90);
-//   double range11 = std::sqrt(4.0 + 4.0), range22 = 2.0;
+  // Add Range-Bearing measurements to from X0 to L0 and X1 to L1.
+  // create a noise model for the landmark measurements
+  auto measurementNoise = noiseModel::Diagonal::Sigmas(
+      Vector2(0.1, 0.2));  // 0.1 rad std on bearing, 20cm on range
+  // create the measurement values - indices are (pose id, landmark id)
+  Rot2 bearing11 = Rot2::fromDegrees(45), bearing22 = Rot2::fromDegrees(90);
+  double range11 = std::sqrt(4.0 + 4.0), range22 = 2.0;
 
-//   // Add Bearing-Range factors
-//   fg.emplace_nonlinear<BearingRangeFactor<Pose2, Point2>>(
-//       X(0), L(0), bearing11, range11, measurementNoise);
-//   fg.emplace_nonlinear<BearingRangeFactor<Pose2, Point2>>(
-//       X(1), L(1), bearing22, range22, measurementNoise);
+  // Add Bearing-Range factors
+  fg.emplace_nonlinear<BearingRangeFactor<Pose2, Point2>>(
+      X(0), L(0), bearing11, range11, measurementNoise);
+  fg.emplace_nonlinear<BearingRangeFactor<Pose2, Point2>>(
+      X(1), L(1), bearing22, range22, measurementNoise);
 
-//   // Create (deliberately inaccurate) initial estimate
-//   Values initialEstimate;
-//   initialEstimate.insert(X(0), Pose2(0.5, 0.0, 0.2));
-//   initialEstimate.insert(X(1), Pose2(2.3, 0.1, -0.2));
-//   initialEstimate.insert(L(0), Point2(1.8, 2.1));
-//   initialEstimate.insert(L(1), Point2(4.1, 1.8));
+  // Create (deliberately inaccurate) initial estimate
+  Values initialEstimate;
+  initialEstimate.insert(X(0), Pose2(0.5, 0.0, 0.2));
+  initialEstimate.insert(X(1), Pose2(2.3, 0.1, -0.2));
+  initialEstimate.insert(L(0), Point2(1.8, 2.1));
+  initialEstimate.insert(L(1), Point2(4.1, 1.8));
 
-//   // We want to eliminate variables not connected to DCFactors first.
-//   Ordering ordering;
-//   ordering += L(0);
-//   ordering += L(1);
-//   ordering += X(0);
-//   ordering += X(1);
+  // We want to eliminate variables not connected to DCFactors first.
+  Ordering ordering;
+  ordering += L(0);
+  ordering += L(1);
+  ordering += X(0);
+  ordering += X(1);
 
-//   HybridGaussianFactorGraph linearized = fg.linearize(initialEstimate);
-//   gtsam::HybridBayesNet::shared_ptr hybridBayesNet;
-//   gtsam::HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
+  HybridGaussianFactorGraph linearized = fg.linearize(initialEstimate);
+  gtsam::HybridBayesNet::shared_ptr hybridBayesNet;
+  gtsam::HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
 
-//   // This should NOT fail
-//   std::tie(hybridBayesNet, remainingFactorGraph) =
-//       linearized.eliminatePartialSequential(ordering);
-//   EXPECT_LONGS_EQUAL(4, hybridBayesNet->size());
-//   EXPECT_LONGS_EQUAL(1, remainingFactorGraph->size());
-// }
+  // This should NOT fail
+  std::tie(hybridBayesNet, remainingFactorGraph) =
+      linearized.eliminatePartialSequential(ordering);
+  EXPECT_LONGS_EQUAL(4, hybridBayesNet->size());
+  EXPECT_LONGS_EQUAL(1, remainingFactorGraph->size());
+}
 
 /* ************************************************************************* */
 int main() {