diff --git a/GPy/models/Bayesian_GPLVM.py b/GPy/models/Bayesian_GPLVM.py
index 430c2718..a18ec9bb 100644
--- a/GPy/models/Bayesian_GPLVM.py
+++ b/GPy/models/Bayesian_GPLVM.py
@@ -85,5 +85,5 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
         return np.hstack((self.dL_dmuS().flatten(), sparse_GP._log_likelihood_gradients(self)))
 
     def plot_latent(self, *args, **kwargs):
-        input_1, input_2 = GPLVM.plot_latent(*args, **kwargs)
-        pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w')
+        input_1, input_2 = GPLVM.plot_latent(self, *args, **kwargs)
+        pb.plot(self.Z[:, input_1], self.Z[:, input_2], '^w')
diff --git a/GPy/models/GPLVM.py b/GPy/models/GPLVM.py
index b44801fc..5be54049 100644
--- a/GPy/models/GPLVM.py
+++ b/GPy/models/GPLVM.py
@@ -81,13 +81,16 @@ class GPLVM(GP):
                     raise ValueError, "cannot Atomatically determine which dimensions to plot, please pass 'which_indices'"
                 k = k[0]
                 if k.name=='rbf':
-                    input_1, input_2 = np.argsort(k.lengthscales)[:2]
+                    input_1, input_2 = np.argsort(k.lengthscale)[:2]
                 elif k.name=='linear':
                     input_1, input_2 = np.argsort(k.variances)[::-1][:2]
 
         #first, plot the output variance as a function of the latent space
         Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(self.X[:,[input_1, input_2]],resolution=resolution)
-        mu, var, low, up = self.predict(Xtest)
+	Xtest_full = np.zeros((Xtest.shape[0], self.X.shape[1]))
+	Xtest_full[:, :2] = Xtest        
+	mu, var, low, up = self.predict(Xtest_full)
+	var = var[:, :2]
         pb.imshow(var.reshape(resolution,resolution).T[::-1,:],extent=[xmin[0],xmax[0],xmin[1],xmax[1]],cmap=pb.cm.binary,interpolation='bilinear')