diff --git a/GPy/examples/oil_flow_demo.py b/GPy/examples/oil_flow_demo.py
index f977e18d..4bcdfa63 100644
--- a/GPy/examples/oil_flow_demo.py
+++ b/GPy/examples/oil_flow_demo.py
@@ -31,7 +31,7 @@ N, D = Y.shape
 Y -= Y.mean(axis=0)
 #Y /= Y.std(axis=0)
 
-Q = 7
+Q = 10
 k = GPy.kern.rbf_ARD(Q) + GPy.kern.white(Q)
 m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M = 12)
 m.constrain_positive('(rbf|bias|S|white|noise)')
diff --git a/GPy/models/BGPLVM.py b/GPy/models/BGPLVM.py
index 2c760ec3..3fc257e9 100644
--- a/GPy/models/BGPLVM.py
+++ b/GPy/models/BGPLVM.py
@@ -57,12 +57,6 @@ class Bayesian_GPLVM(sparse_GP_regression, GPLVM):
 
         return np.hstack((dL_dmu.flatten(), dL_dS.flatten()))
 
-
     def log_likelihood_gradients(self):
         return np.hstack((self.dL_dmuS().flatten(), sparse_GP_regression.log_likelihood_gradients(self)))
 
-    def plot(self):
-        GPLVM.plot(self)
-        #passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
-        mu, var = sparse_GP_regression.predict(self, self.Z+np.random.randn(*self.Z.shape)*0.0001)
-        pb.plot(mu[:, 0] , mu[:, 1], 'ko')