diff --git a/GPy/models/BGPLVM.py b/GPy/models/BGPLVM.py
index db147944..ffa7df54 100644
--- a/GPy/models/BGPLVM.py
+++ b/GPy/models/BGPLVM.py
@@ -5,10 +5,12 @@ import numpy as np
 import pylab as pb
 import sys, pdb
 from GPLVM import GPLVM
-from sparse_GP_regression import sparse_GP_regression
+from sparse_GP import sparse_GP
 from GPy.util.linalg import pdinv
+from ..likelihoods import Gaussian
+from .. import kern
 
-class Bayesian_GPLVM(sparse_GP_regression, GPLVM):
+class Bayesian_GPLVM(sparse_GP, GPLVM):
     """
     Bayesian Gaussian Process Latent Variable Model
 
@@ -20,15 +22,23 @@ class Bayesian_GPLVM(sparse_GP_regression, GPLVM):
     :type init: 'PCA'|'random'
 
     """
-    def __init__(self, Y, Q, init='PCA', **kwargs):
+    def __init__(self, Y, Q, init='PCA', M=10, Z=None, **kwargs):
         X = self.initialise_latent(init, Q, Y)
-        S = np.ones_like(X) * 1e-2# 
-        sparse_GP_regression.__init__(self, X, Y, X_uncertainty = S, **kwargs)
+
+        if Z is None:
+            Z = np.random.permutation(X.copy())[:M]
+        else:
+            assert Z.shape[1]==X.shape[1]
+
+        kernel = kern.rbf(Q) + kern.white(Q)
+
+        S = np.ones_like(X) * 1e-2#
+        sparse_GP.__init__(self, X, Gaussian(Y), X_uncertainty = S, Z=Z,**kwargs)
 
     def _get_param_names(self):
         X_names = sum([['X_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[])
         S_names = sum([['S_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[])
-        return (X_names + S_names + sparse_GP_regression._get_param_names(self))
+        return (X_names + S_names + sparse_GP._get_param_names(self))
 
     def _get_params(self):
         """
@@ -40,13 +50,13 @@ class Bayesian_GPLVM(sparse_GP_regression, GPLVM):
         ===============================================================
 
         """
-        return np.hstack((self.X.flatten(), self.X_uncertainty.flatten(), sparse_GP_regression._get_params(self)))
+        return np.hstack((self.X.flatten(), self.X_uncertainty.flatten(), sparse_GP._get_params(self)))
 
     def _set_params(self,x):
         N, Q = self.N, self.Q
         self.X = x[:self.X.size].reshape(N,Q).copy()
         self.X_uncertainty = x[(N*Q):(2*N*Q)].reshape(N,Q).copy()
-        sparse_GP_regression._set_params(self, x[(2*N*Q):])
+        sparse_GP._set_params(self, x[(2*N*Q):])
 
     def dL_dmuS(self):
         dL_dmu_psi0, dL_dS_psi0 = self.kern.dpsi1_dmuS(self.dL_dpsi1,self.Z,self.X,self.X_uncertainty)
@@ -58,5 +68,5 @@ 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)))
+        return np.hstack((self.dL_dmuS().flatten(), sparse_GP._log_likelihood_gradients(self)))
 
diff --git a/GPy/models/__init__.py b/GPy/models/__init__.py
index 2269610d..9cc8fa68 100644
--- a/GPy/models/__init__.py
+++ b/GPy/models/__init__.py
@@ -9,6 +9,6 @@ from sparse_GP_regression import sparse_GP_regression
 from GPLVM import GPLVM
 from warped_GP import warpedGP
 # TODO: from generalized_FITC import generalized_FITC
-#from sparse_GPLVM import sparse_GPLVM
+from sparse_GPLVM import sparse_GPLVM
 #from uncollapsed_sparse_GP import uncollapsed_sparse_GP
-#from BGPLVM import Bayesian_GPLVM
+from BGPLVM import Bayesian_GPLVM
diff --git a/GPy/models/sparse_GP.py b/GPy/models/sparse_GP.py
index 7252f085..f35b3918 100644
--- a/GPy/models/sparse_GP.py
+++ b/GPy/models/sparse_GP.py
@@ -65,7 +65,7 @@ class sparse_GP(GP):
         # kernel computations, using BGPLVM notation
         self.Kmm = self.kern.K(self.Z)
         if self.has_uncertain_inputs:
-            self.psi0 = self.kern.psi0(self.Z,self.X, self.X_uncerTainty)
+            self.psi0 = self.kern.psi0(self.Z,self.X, self.X_uncertainty)
             self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty).T
             self.psi2 = self.kern.psi2(self.Z,self.X, self.X_uncertainty)
             if self.likelihood.is_heteroscedastic: