diff --git a/gtsam/hybrid/HybridBayesNet.cpp b/gtsam/hybrid/HybridBayesNet.cpp
index bc0d8e95e..be9cdba85 100644
--- a/gtsam/hybrid/HybridBayesNet.cpp
+++ b/gtsam/hybrid/HybridBayesNet.cpp
@@ -293,21 +293,21 @@ HybridValues HybridBayesNet::sample() const {
 /* ************************************************************************* */
 AlgebraicDecisionTree<Key> HybridBayesNet::logProbability(
     const VectorValues &continuousValues) const {
-  AlgebraicDecisionTree<Key> error_tree(0.0);
+  AlgebraicDecisionTree<Key> result(0.0);
 
   // Iterate over each conditional.
   for (auto &&conditional : *this) {
     if (auto gm = conditional->asMixture()) {
       // If conditional is hybrid, select based on assignment and compute
       // logProbability.
-      error_tree = error_tree + gm->logProbability(continuousValues);
+      result = result + gm->logProbability(continuousValues);
     } else if (auto gc = conditional->asGaussian()) {
       // If continuous, get the (double) logProbability and add it to the
-      // error_tree
+      // result
       double logProbability = gc->logProbability(continuousValues);
       // Add the computed logProbability to every leaf of the logProbability
       // tree.
-      error_tree = error_tree.apply([logProbability](double leaf_value) {
+      result = result.apply([logProbability](double leaf_value) {
         return leaf_value + logProbability;
       });
     } else if (auto dc = conditional->asDiscrete()) {
@@ -317,7 +317,7 @@ AlgebraicDecisionTree<Key> HybridBayesNet::logProbability(
     }
   }
 
-  return error_tree;
+  return result;
 }
 
 /* ************************************************************************* */
diff --git a/gtsam/inference/BayesNet.h b/gtsam/inference/BayesNet.h
index 4d266df46..3e6d55281 100644
--- a/gtsam/inference/BayesNet.h
+++ b/gtsam/inference/BayesNet.h
@@ -54,9 +54,11 @@ class BayesNet : public FactorGraph<CONDITIONAL> {
 
   /**
    * Constructor that takes an initializer list of shared pointers.
-   *  BayesNet<SymbolicConditional> bn = {make_shared<SymbolicConditional>(), ...};
+   *  BayesNet<SymbolicConditional> bn = {make_shared<SymbolicConditional>(),
+   * ...};
    */
-  BayesNet(std::initializer_list<sharedConditional> conditionals): Base(conditionals) {}
+  BayesNet(std::initializer_list<sharedConditional> conditionals)
+      : Base(conditionals) {}
 
   /// @}
 
@@ -91,7 +93,10 @@ class BayesNet : public FactorGraph<CONDITIONAL> {
   /// @name HybridValues methods
   /// @{
 
+  // Expose HybridValues version of logProbability.
   double logProbability(const HybridValues& x) const;
+
+  // Expose HybridValues version of evaluate.
   double evaluate(const HybridValues& c) const;
 
   /// @}
diff --git a/python/gtsam/tests/test_GaussianBayesNet.py b/python/gtsam/tests/test_GaussianBayesNet.py
index 9065c7bee..05522441b 100644
--- a/python/gtsam/tests/test_GaussianBayesNet.py
+++ b/python/gtsam/tests/test_GaussianBayesNet.py
@@ -10,9 +10,6 @@ Author: Frank Dellaert
 """
 # pylint: disable=invalid-name, no-name-in-module, no-member
 
-from __future__ import print_function
-
-import math
 import unittest
 
 import numpy as np
@@ -55,9 +52,9 @@ class TestGaussianBayesNet(GtsamTestCase):
         values.insert(_y_, np.array([5.0]))
         for i in [0, 1]:
             self.assertAlmostEqual(bayesNet.at(i).logProbability(values),
-                                   math.log(bayesNet.at(i).evaluate(values)))
+                                   np.log(bayesNet.at(i).evaluate(values)))
         self.assertAlmostEqual(bayesNet.logProbability(values),
-                               math.log(bayesNet.evaluate(values)))
+                               np.log(bayesNet.evaluate(values)))
 
     def test_sample(self):
         """Test sample method"""