diff --git a/gtsam/nonlinear/nonlinear.i b/gtsam/nonlinear/nonlinear.i
index eedf421bc..055fbd75b 100644
--- a/gtsam/nonlinear/nonlinear.i
+++ b/gtsam/nonlinear/nonlinear.i
@@ -98,11 +98,11 @@ class NonlinearFactorGraph {
   string dot(
       const gtsam::Values& values,
       const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter,
-      const GraphvizFormatting& formatting = GraphvizFormatting());
+      const GraphvizFormatting& writer = GraphvizFormatting());
   void saveGraph(
       const string& s, const gtsam::Values& values,
       const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter,
-      const GraphvizFormatting& formatting = GraphvizFormatting()) const;
+      const GraphvizFormatting& writer = GraphvizFormatting()) const;
 
   // enabling serialization functionality
   void serialize() const;
diff --git a/python/gtsam/tests/test_GaussianBayesNet.py b/python/gtsam/tests/test_GaussianBayesNet.py
new file mode 100644
index 000000000..8cdbec0af
--- /dev/null
+++ b/python/gtsam/tests/test_GaussianBayesNet.py
@@ -0,0 +1,53 @@
+"""
+GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
+Atlanta, Georgia 30332-0415
+All Rights Reserved
+
+See LICENSE for the license information
+
+Unit tests for Linear Factor Graphs.
+Author: Frank Dellaert & Gerry Chen
+"""
+# pylint: disable=invalid-name, no-name-in-module, no-member
+
+from __future__ import print_function
+
+import unittest
+
+import gtsam
+import numpy as np
+from gtsam import GaussianBayesNet, GaussianConditional
+from gtsam.utils.test_case import GtsamTestCase
+
+# some keys
+_x_ = 11
+_y_ = 22
+_z_ = 33
+
+
+def smallBayesNet():
+    """Create a small Bayes Net for testing"""
+    bayesNet = GaussianBayesNet()
+    I_1x1 = np.eye(1, dtype=float)
+    bayesNet.push_back(GaussianConditional(
+        _x_, [9.0], I_1x1, _y_, I_1x1))
+    bayesNet.push_back(GaussianConditional(_y_, [5.0], I_1x1))
+    return bayesNet
+
+
+class TestGaussianBayesNet(GtsamTestCase):
+    """Tests for Gaussian Bayes nets."""
+
+    def test_matrix(self):
+        """Test matrix method"""
+        R, d = smallBayesNet().matrix()  # get matrix and RHS
+        R1 = np.array([
+            [1.0, 1.0],
+            [0.0, 1.0]])
+        d1 = np.array([9.0, 5.0])
+        np.testing.assert_equal(R, R1)
+        np.testing.assert_equal(d, d1)
+
+
+if __name__ == '__main__':
+    unittest.main()