diff --git a/python/gtsam_examples/ImuFactorExample.py b/python/gtsam_examples/ImuFactorExample.py
index c1181d980..ca5e524ee 100644
--- a/python/gtsam_examples/ImuFactorExample.py
+++ b/python/gtsam_examples/ImuFactorExample.py
@@ -22,12 +22,18 @@ class ImuFactorExample(PreintegrationExample):
 
     def __init__(self):
         self.velocity = np.array([2, 0, 0])
-        forward_twist = (np.zeros(3), self.velocity)
-        loop_twist = (np.array([0, -math.radians(30), 0]), self.velocity)
-        super(ImuFactorExample, self).__init__(loop_twist)
         self.priorNoise = gtsam.noiseModel.Isotropic.Sigma(6, 0.1)
         self.velNoise = gtsam.noiseModel.Isotropic.Sigma(3, 0.1)
-    
+
+        forward_twist = (np.zeros(3), self.velocity)
+        loop_twist = (np.array([0, -math.radians(30), 0]), self.velocity)
+
+        accBias = np.array([-0.3, 0.1, 0.2])
+        gyroBias = np.array([0.1, 0.3, -0.1])
+        bias = gtsam.ConstantBias(accBias, gyroBias)
+
+        super(ImuFactorExample, self).__init__(loop_twist, bias)
+
     def addPrior(self, i, graph):
         state = self.scenario.navState(i)
         graph.push_back(gtsam.PriorFactorPose3(X(i), state.pose(), self.priorNoise))
@@ -62,11 +68,6 @@ class ImuFactorExample(PreintegrationExample):
             if (k + 1) % 100 == 0:
                 factor = gtsam.ImuFactor(X(i), V(i), X(i + 1), V(i + 1), BIAS_KEY, pim)
                 graph.push_back(factor)
-                H1 = gtsam.OptionalJacobian9()
-                H2 = gtsam.OptionalJacobian96()
-                predicted_state_j = pim.predict(actual_state_i, self.actualBias, H1, H2)
-                error = pim.computeError(actual_state_i, predicted_state_j, self.actualBias, H1, H1, H2)
-                print("error={}, norm ={}".format(error, np.linalg.norm(error)))
                 pim.resetIntegration()
                 actual_state_i = self.scenario.navState(t + self.dt)
                 i += 1
@@ -76,33 +77,26 @@ class ImuFactorExample(PreintegrationExample):
         self.addPrior(0, graph)
         self.addPrior(num_poses - 1, graph)
         
-#         graph.print("\Graph:\n")
-
         initial = gtsam.Values()
         initial.insert(BIAS_KEY, self.actualBias)
         for i in range(num_poses):
             state_i = self.scenario.navState(float(i))
-            plotPose3(POSES_FIG, state_i.pose(), 0.9)
             initial.insert(X(i), state_i.pose())
             initial.insert(V(i), state_i.velocity())
         
-        for idx in range(num_poses - 1):
-            ff = gtsam.getNonlinearFactor(graph, idx)
-            print(ff.error(initial))
-
         # optimize using Levenberg-Marquardt optimization
         params = gtsam.LevenbergMarquardtParams()
         params.setVerbosityLM("SUMMARY")
         optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial, params)
         result = optimizer.optimize()
-        result.print("\Result:\n")
 
-        # Plot cameras
+        # Plot resulting poses
         i = 0
         while result.exists(X(i)):
-            pose_i = result.pose3_at(X(i))
+            pose_i = result.atPose3(X(i))
             plotPose3(POSES_FIG, pose_i, 0.1)
             i += 1
+        print(result.atConstantBias(BIAS_KEY))
             
         plt.ioff()
         plt.show()
diff --git a/python/gtsam_examples/PreintegrationExample.py b/python/gtsam_examples/PreintegrationExample.py
index 808063a94..7095dc59e 100644
--- a/python/gtsam_examples/PreintegrationExample.py
+++ b/python/gtsam_examples/PreintegrationExample.py
@@ -27,7 +27,7 @@ class PreintegrationExample(object):
         params.integrationCovariance = 0.0000001 ** 2 * np.identity(3, np.float)
         return params
 
-    def __init__(self, twist=None):
+    def __init__(self, twist=None, bias=None):
         """Initialize with given twist, a pair(angularVelocityVector, velocityVector)."""
         
         # setup interactive plotting
@@ -53,9 +53,14 @@ class PreintegrationExample(object):
         self.g = 10  # simple gravity constant
         self.params = self.defaultParams(self.g)
         ptr = gtsam.ScenarioPointer(self.scenario)
-        accBias = np.array([0, 0.1, 0])
-        gyroBias = np.array([0, 0, 0])
-        self.actualBias = gtsam.ConstantBias(accBias, gyroBias)
+
+        if bias is not None:
+            self.actualBias = bias
+        else:
+            accBias = np.array([0, 0.1, 0])
+            gyroBias = np.array([0, 0, 0])
+            self.actualBias = gtsam.ConstantBias(accBias, gyroBias)
+
         self.runner = gtsam.ScenarioRunner(ptr, self.params, self.dt, self.actualBias)
 
     def plotImu(self, t, measuredOmega, measuredAcc):