diff --git a/GPy/core/parameterization/variational.py b/GPy/core/parameterization/variational.py
index d0fd83d2..5412a70d 100644
--- a/GPy/core/parameterization/variational.py
+++ b/GPy/core/parameterization/variational.py
@@ -46,9 +46,15 @@ class SpikeAndSlabPrior(VariationalPrior):
         mu = variational_posterior.mean
         S = variational_posterior.variance
         gamma = variational_posterior.binary_prob
+        if len(self.pi.shape)==2:
+            idx = np.unique(gamma._raveled_index()/gamma.shape[-1])
+            pi = self.pi[idx]
+        else:
+            pi = self.pi
+            
         var_mean = np.square(mu)/self.variance
         var_S = (S/self.variance - np.log(S))
-        var_gamma = (gamma*np.log(gamma/self.pi)).sum()+((1-gamma)*np.log((1-gamma)/(1-self.pi))).sum()
+        var_gamma = (gamma*np.log(gamma/pi)).sum()+((1-gamma)*np.log((1-gamma)/(1-pi))).sum()
         return var_gamma+ (gamma* (np.log(self.variance)-1. +var_mean + var_S)).sum()/2.
 
     def update_gradients_KL(self, variational_posterior):
diff --git a/GPy/core/sparse_gp_mpi.py b/GPy/core/sparse_gp_mpi.py
index 9d28f0a0..588ed61c 100644
--- a/GPy/core/sparse_gp_mpi.py
+++ b/GPy/core/sparse_gp_mpi.py
@@ -95,12 +95,12 @@ class SparseGP_MPI(SparseGP):
             super(SparseGP_MPI, self).optimize(optimizer,start,**kwargs)
             self.mpi_comm.Bcast(np.int32(-1),root=0)
         elif self.mpi_comm.rank>0:
-            x = self._get_params_transformed().copy()
+            x = self.optimizer_array.copy()
             flag = np.empty(1,dtype=np.int32)
             while True:
                 self.mpi_comm.Bcast(flag,root=0)
                 if flag==1:
-                    self._set_params_transformed(x)
+                    self.optimizer_array = x
                 elif flag==-1:
                     break
                 else:
@@ -109,5 +109,8 @@ class SparseGP_MPI(SparseGP):
         self._IN_OPTIMIZATION_ = False
 
     def parameters_changed(self):
-        update_gradients(self, mpi_comm=self.mpi_comm)
+        if isinstance(self.inference_method,VarDTC_minibatch):
+            update_gradients(self, mpi_comm=self.mpi_comm)
+        else:
+            super(SparseGP_MPI,self).parameters_changed()
 
diff --git a/GPy/models/ss_gplvm.py b/GPy/models/ss_gplvm.py
index 1b06ce32..dcb1fe34 100644
--- a/GPy/models/ss_gplvm.py
+++ b/GPy/models/ss_gplvm.py
@@ -84,18 +84,17 @@ class SSGPLVM(SparseGP_MPI):
         """Get the gradients of the posterior distribution of X in its specific form."""
         return X.mean.gradient, X.variance.gradient, X.binary_prob.gradient
 
-#     def parameters_changed(self):
-#         if isinstance(self.inference_method, VarDTC_GPU) or isinstance(self.inference_method, VarDTC_minibatch):
-#             update_gradients(self, mpi_comm=self.mpi_comm)
-#             return
-#         
-#         super(SSGPLVM, self).parameters_changed()
-#         self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
-# 
-#         self.X.mean.gradient, self.X.variance.gradient, self.X.binary_prob.gradient = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, dL_dpsi0=self.grad_dict['dL_dpsi0'], dL_dpsi1=self.grad_dict['dL_dpsi1'], dL_dpsi2=self.grad_dict['dL_dpsi2'])
-# 
-#         # update for the KL divergence
-#         self.variational_prior.update_gradients_KL(self.X)
+    def parameters_changed(self):
+        super(SSGPLVM,self).parameters_changed()
+        if isinstance(self.inference_method, VarDTC_minibatch):
+            return
+        
+        self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
+
+        self.X.mean.gradient, self.X.variance.gradient, self.X.binary_prob.gradient = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, dL_dpsi0=self.grad_dict['dL_dpsi0'], dL_dpsi1=self.grad_dict['dL_dpsi1'], dL_dpsi2=self.grad_dict['dL_dpsi2'])
+
+        # update for the KL divergence
+        self.variational_prior.update_gradients_KL(self.X)
 
     def input_sensitivity(self):
         if self.kern.ARD: