diff --git a/GPy/core/parameterization/variational.py b/GPy/core/parameterization/variational.py
index d1c0faf8..05ce2109 100644
--- a/GPy/core/parameterization/variational.py
+++ b/GPy/core/parameterization/variational.py
@@ -63,7 +63,7 @@ class NormalPosterior(VariationalPosterior):
         from ...plotting.matplot_dep import variational_plots
         return variational_plots.plot(self,*args)
 
-class SpikeAndSlab(VariationalPosterior):
+class SpikeAndSlabPosterior(VariationalPosterior):
     '''
     The SpikeAndSlab distribution for variational approximations.
     '''
@@ -71,7 +71,7 @@ class SpikeAndSlab(VariationalPosterior):
         """
         binary_prob : the probability of the distribution on the slab part.
         """
-        super(SpikeAndSlab, self).__init__(means, variances, name)
+        super(SpikeAndSlabPosterior, self).__init__(means, variances, name)
         self.gamma = Param("binary_prob",binary_prob,)
         self.add_parameter(self.gamma)
 
diff --git a/GPy/kern/_src/rbf.py b/GPy/kern/_src/rbf.py
index 0c8588a2..e23e9e2c 100644
--- a/GPy/kern/_src/rbf.py
+++ b/GPy/kern/_src/rbf.py
@@ -182,7 +182,7 @@ class RBF(Kern):
 
         return grad
 
-    def gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
+    def update_gradients_q_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, posterior_variational):
         mu = posterior_variational.mean
         S = posterior_variational.variance
         self._psi_computations(Z, mu, S)
@@ -194,8 +194,9 @@ class RBF(Kern):
         tmp = self._psi2[:, :, :, None] / self.lengthscale2 / self._psi2_denom
         grad_mu += -2.*(dL_dpsi2[:, :, :, None] * tmp * self._psi2_mudist).sum(1).sum(1)
         grad_S += (dL_dpsi2[:, :, :, None] * tmp * (2.*self._psi2_mudist_sq - 1)).sum(1).sum(1)
-
-        return grad_mu, grad_S
+        
+        posterior_variational.mean.gradient = grad_mu
+        posterior_variational.variance.gradient = grad_S
 
     def gradients_X(self, dL_dK, X, X2=None):
         #if self._X is None or X.base is not self._X.base or X2 is not None:
diff --git a/GPy/models/bayesian_gplvm.py b/GPy/models/bayesian_gplvm.py
index 7b09e0b1..a8d643b9 100644
--- a/GPy/models/bayesian_gplvm.py
+++ b/GPy/models/bayesian_gplvm.py
@@ -63,9 +63,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
         super(BayesianGPLVM, self).parameters_changed()
         self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.q)
         
-        # TODO: This has to go into kern
-        # maybe a update_gradients_q_variational?
-        self.q.mean.gradient, self.q.variance.gradient = self.kern.gradients_q_variational(posterior_variational=self.q, Z=self.Z, **self.grad_dict)
+        self.kern.update_gradients_q_variational(posterior_variational=self.q, Z=self.Z, **self.grad_dict)
         
         # update for the KL divergence
         self.variational_prior.update_gradients_KL(self.q)