diff --git a/gtsam/hybrid/hybrid.i b/gtsam/hybrid/hybrid.i
index 87aa3bc14..e877e5ee7 100644
--- a/gtsam/hybrid/hybrid.i
+++ b/gtsam/hybrid/hybrid.i
@@ -135,29 +135,9 @@ class HybridBayesTree {
 #include <gtsam/hybrid/HybridBayesNet.h>
 class HybridBayesNet {
   HybridBayesNet();
-  void add(const gtsam::HybridConditional& s);
-  void addMixture(const gtsam::GaussianMixture* s);
-  void addGaussian(const gtsam::GaussianConditional* s);
-  void addDiscrete(const gtsam::DiscreteConditional* s);
-
-  void emplaceMixture(const gtsam::GaussianMixture& s);
-  void emplaceMixture(const gtsam::KeyVector& continuousFrontals,
-                      const gtsam::KeyVector& continuousParents,
-                      const gtsam::DiscreteKeys& discreteParents,
-                      const std::vector<gtsam::GaussianConditional::shared_ptr>&
-                          conditionalsList);
-  void emplaceGaussian(const gtsam::GaussianConditional& s);
-  void emplaceDiscrete(const gtsam::DiscreteConditional& s);
-  void emplaceDiscrete(const gtsam::DiscreteKey& key, string spec);
-  void emplaceDiscrete(const gtsam::DiscreteKey& key,
-                       const gtsam::DiscreteKeys& parents, string spec);
-  void emplaceDiscrete(const gtsam::DiscreteKey& key,
-                       const std::vector<gtsam::DiscreteKey>& parents,
-                       string spec);
-
-  gtsam::GaussianMixture* atMixture(size_t i) const;
-  gtsam::GaussianConditional* atGaussian(size_t i) const;
-  gtsam::DiscreteConditional* atDiscrete(size_t i) const;
+  void push_back(const gtsam::GaussianMixture* s);
+  void push_back(const gtsam::GaussianConditional* s);
+  void push_back(const gtsam::DiscreteConditional* s);
 
   bool empty() const;
   size_t size() const;
diff --git a/python/gtsam/tests/test_HybridBayesNet.py b/python/gtsam/tests/test_HybridBayesNet.py
index af89a4ba7..75a2e9f8b 100644
--- a/python/gtsam/tests/test_HybridBayesNet.py
+++ b/python/gtsam/tests/test_HybridBayesNet.py
@@ -16,13 +16,13 @@ import numpy as np
 from gtsam.symbol_shorthand import A, X
 from gtsam.utils.test_case import GtsamTestCase
 
-import gtsam
-from gtsam import (DiscreteKeys, GaussianConditional, GaussianMixture,
+from gtsam import (DiscreteKeys, GaussianMixture, DiscreteConditional, GaussianConditional, GaussianMixture,
                    HybridBayesNet, HybridValues, noiseModel)
 
 
 class TestHybridBayesNet(GtsamTestCase):
     """Unit tests for HybridValues."""
+
     def test_evaluate(self):
         """Test evaluate for a hybrid Bayes net P(X0|X1) P(X1|Asia) P(Asia)."""
         asiaKey = A(0)
@@ -40,15 +40,15 @@ class TestHybridBayesNet(GtsamTestCase):
         # Create the conditionals
         conditional0 = GaussianConditional(X(1), [5], I_1x1, model0)
         conditional1 = GaussianConditional(X(1), [2], I_1x1, model1)
-        dkeys = DiscreteKeys()
-        dkeys.push_back(Asia)
-        gm = GaussianMixture([X(1)], [], dkeys, [conditional0, conditional1]) 
+        discrete_keys = DiscreteKeys()
+        discrete_keys.push_back(Asia)
 
         # Create hybrid Bayes net.
         bayesNet = HybridBayesNet()
-        bayesNet.addGaussian(gc)
-        bayesNet.addMixture(gm)
-        bayesNet.emplaceDiscrete(Asia, "99/1")
+        bayesNet.push_back(gc)
+        bayesNet.push_back(GaussianMixture(
+            [X(1)], [], discrete_keys, [conditional0, conditional1]))
+        bayesNet.push_back(DiscreteConditional(Asia, "99/1"))
 
         # Create values at which to evaluate.
         values = HybridValues()
diff --git a/python/gtsam/tests/test_HybridFactorGraph.py b/python/gtsam/tests/test_HybridFactorGraph.py
index f83b95442..956a6f572 100644
--- a/python/gtsam/tests/test_HybridFactorGraph.py
+++ b/python/gtsam/tests/test_HybridFactorGraph.py
@@ -108,16 +108,16 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
                                                                  I_1x1,
                                                                  X(0), [0],
                                                                  sigma=3)
-            bayesNet.emplaceMixture([Z(i)], [X(0)], keys,
-                                    [conditional0, conditional1])
+            bayesNet.push_back(GaussianMixture([Z(i)], [X(0)], keys,
+                                               [conditional0, conditional1]))
 
         # Create prior on X(0).
         prior_on_x0 = GaussianConditional.FromMeanAndStddev(
             X(0), [prior_mean], prior_sigma)
-        bayesNet.addGaussian(prior_on_x0)
+        bayesNet.push_back(prior_on_x0)
 
         # Add prior on mode.
-        bayesNet.emplaceDiscrete(mode, "4/6")
+        bayesNet.push_back(DiscreteConditional(mode, "4/6"))
 
         return bayesNet
 
@@ -163,11 +163,11 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         fg = HybridGaussianFactorGraph()
         num_measurements = bayesNet.size() - 2
         for i in range(num_measurements):
-            conditional = bayesNet.atMixture(i)
+            conditional = bayesNet.at(i).asMixture()
             factor = conditional.likelihood(cls.measurements(sample, [i]))
             fg.push_back(factor)
-        fg.push_back(bayesNet.atGaussian(num_measurements))
-        fg.push_back(bayesNet.atDiscrete(num_measurements+1))
+        fg.push_back(bayesNet.at(num_measurements).asGaussian())
+        fg.push_back(bayesNet.at(num_measurements+1).asDiscrete())
         return fg
 
     @classmethod