diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index 1a9a9dcc5..3bafe5a9c 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -139,27 +139,16 @@ DecisionTreeFactor HybridBayesNet::pruneDiscreteConditionals(
   for (size_t i = 0; i < this->size(); i++) {
     auto conditional = this->at(i);
     if (conditional->isDiscrete()) {
-      std::cout << ">>>" << std::endl;
-      conditional->print();
       discreteProbs = discreteProbs * (*conditional->asDiscrete());
-      // discreteProbs.print();
-      // std::cout << "================\n" << std::endl;
 
       Ordering conditional_keys(conditional->frontals());
       discrete_frontals += conditional_keys;
       discrete_factor_idxs.push_back(i);
     }
   }
-  std::cout << "Original Joint Prob:" << std::endl;
-  std::cout << discreteProbs.nrAssignments() << std::endl;
-  discreteProbs.print();
+
   const DecisionTreeFactor prunedDiscreteProbs =
       discreteProbs.prune(maxNrLeaves);
-  std::cout << "Pruned Joint Prob:" << std::endl;
-  std::cout << prunedDiscreteProbs.nrAssignments() << std::endl;
-  prunedDiscreteProbs.print();
-  std::cout << "\n\n\n";
-  gttoc_(HybridBayesNet_PruneDiscreteConditionals);
 
   // Eliminate joint probability back into conditionals
   DiscreteFactorGraph dfg{prunedDiscreteProbs};
@@ -168,8 +157,6 @@ DecisionTreeFactor HybridBayesNet::pruneDiscreteConditionals(
   // Assign pruned discrete conditionals back at the correct indices.
   for (size_t i = 0; i < discrete_factor_idxs.size(); i++) {
     size_t idx = discrete_factor_idxs.at(i);
-    // std::cout << i << std::endl;
-    // dbn->at(i)->print();
     this->at(idx) = std::make_shared<HybridConditional>(dbn->at(i));
   }
 
diff --git a/gtsam/hybrid/HybridGaussianFactorGraph.cpp b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
index ee36ecc87..191e13d76 100644
--- a/gtsam/hybrid/HybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
@@ -96,7 +96,6 @@ static GaussianFactorGraphTree addGaussian(
 // TODO(dellaert): it's probably more efficient to first collect the discrete
 // keys, and then loop over all assignments to populate a vector.
 GaussianFactorGraphTree HybridGaussianFactorGraph::assembleGraphTree() const {
-
   GaussianFactorGraphTree result;
 
   for (auto &f : factors_) {
@@ -175,7 +174,7 @@ discreteElimination(const HybridGaussianFactorGraph &factors,
       throwRuntimeError("continuousElimination", f);
     }
   }
-  dfg.print("The DFG to eliminate");
+
   // NOTE: This does sum-product. For max-product, use EliminateForMPE.
   auto result = EliminateDiscrete(dfg, frontalKeys);