diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index 90951b074..0ff4e342b 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -329,34 +329,29 @@ HybridValues HybridBayesNet::optimize() const {
         return gbn.logNormalizationConstant();
       });
 
-  // Compute unnormalized error term
-  std::vector<DiscreteKey> labels;
-  for (auto &&key : x_map.labels()) {
-    labels.push_back(std::make_pair(key, 2));
-  }
+  // Compute errors as VectorValues
+  DecisionTree<Key, VectorValues> errorVectors = x_map.apply(
+      [this](const Assignment<Key> &assignment, const VectorValues &mu) {
+        double error = 0.0;
+        for (auto &&f : *this) {
+          if (auto gm = dynamic_pointer_cast<GaussianMixture>(f)) {
+            error += gm->error(HybridValues(mu, DiscreteValues(assignment)));
 
-  std::vector<double> errors;
-  x_map.visitWith([this, &errors](const Assignment<Key> &assignment,
-                                  const VectorValues &mu) {
-    double error = 0.0;
-    for (auto &&f : *this) {
-      if (auto gm = dynamic_pointer_cast<GaussianMixture>(f)) {
-        error += gm->error(HybridValues(mu, DiscreteValues(assignment)));
+          } else if (auto hc = dynamic_pointer_cast<HybridConditional>(f)) {
+            if (auto gm = hc->asMixture()) {
+              error += gm->error(HybridValues(mu, DiscreteValues(assignment)));
 
-      } else if (auto hc = dynamic_pointer_cast<HybridConditional>(f)) {
-        if (auto gm = hc->asMixture()) {
-          error += gm->error(HybridValues(mu, DiscreteValues(assignment)));
-
-        } else if (auto g = hc->asGaussian()) {
-          error += g->error(mu);
+            } else if (auto g = hc->asGaussian()) {
+              error += g->error(mu);
+            }
+          }
         }
-      }
-    }
-    errors.push_back(error);
-  });
-
-  AlgebraicDecisionTree<Key> errorTree =
-      DecisionTree<Key, double>(labels, errors);
+        VectorValues e;
+        e.insert(0, Vector1(error));
+        return e;
+      });
+  AlgebraicDecisionTree<Key> errorTree = DecisionTree<Key, double>(
+      errorVectors, [](const VectorValues &v) { return v[0](0); });
 
   // Compute model selection term (with help from ADT methods)
   AlgebraicDecisionTree<Key> model_selection_term =