diff --git a/GPy/models/bayesian_gplvm_minibatch.py b/GPy/models/bayesian_gplvm_minibatch.py
index cc17e9ee..2a457a21 100644
--- a/GPy/models/bayesian_gplvm_minibatch.py
+++ b/GPy/models/bayesian_gplvm_minibatch.py
@@ -40,12 +40,13 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
             Z = np.random.permutation(X.copy())[:num_inducing]
         assert Z.shape[1] == X.shape[1]
 
-        if X_variance == False:
+        if X_variance is False:
             self.logger.info('no variance on X, activating sparse GPLVM')
             X = Param("latent space", X)
-        elif X_variance is None:
-            self.logger.info("initializing latent space variance ~ uniform(0,.1)")
-            X_variance = np.random.uniform(0,.1,X.shape)
+        else:
+            if X_variance is None:
+                self.logger.info("initializing latent space variance ~ uniform(0,.1)")
+                X_variance = np.random.uniform(0,.1,X.shape)
             self.variational_prior = NormalPrior()
             X = NormalPosterior(X, X_variance)
 
@@ -71,13 +72,13 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
         self.X = X
         self.link_parameter(self.X, 0)
 
-    def set_X_gradients(self, X, X_grad):
-        """Set the gradients of the posterior distribution of X in its specific form."""
-        X.mean.gradient, X.variance.gradient = X_grad
+    #def set_X_gradients(self, X, X_grad):
+    #    """Set the gradients of the posterior distribution of X in its specific form."""
+    #    X.mean.gradient, X.variance.gradient = X_grad
 
-    def get_X_gradients(self, X):
-        """Get the gradients of the posterior distribution of X in its specific form."""
-        return X.mean.gradient, X.variance.gradient
+    #def get_X_gradients(self, X):
+    #    """Get the gradients of the posterior distribution of X in its specific form."""
+    #    return X.mean.gradient, X.variance.gradient
 
     def _outer_values_update(self, full_values):
         """
@@ -122,7 +123,7 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
 
             if self.missing_data or not self.stochastics:
                 self._log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(self.X)
-            elif self.stochastics:
+            else: #self.stochastics is given:
                 d = self.output_dim
                 self._log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(self.X)*self.stochastics.batchsize/d
 
diff --git a/GPy/testing/minibatch_tests.py b/GPy/testing/minibatch_tests.py
index d217cb16..fbf12939 100644
--- a/GPy/testing/minibatch_tests.py
+++ b/GPy/testing/minibatch_tests.py
@@ -54,7 +54,7 @@ class BGPLVMTest(unittest.TestCase):
     def test_lik_comparisons_m0_s0(self):
         # Test if the different implementations give the exact same likelihood as the full model.
         # All of the following settings should give the same likelihood and gradients as the full model:
-        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=False, stochastic=False)
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=self.m_full.X.variance.values, missing_data=False, stochastic=False)
         m[:] = self.m_full[:]
         np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
         np.testing.assert_allclose(m.gradient, self.m_full.gradient)