diff --git a/GPy/core/parameterization/variational.py b/GPy/core/parameterization/variational.py
index cd2ecf53..3137256f 100644
--- a/GPy/core/parameterization/variational.py
+++ b/GPy/core/parameterization/variational.py
@@ -63,10 +63,6 @@ class GmmNormalPrior(VariationalPrior):
     def KL_divergence(self, variational_posterior):
         # Lagrange multiplier maybe also needed here
 
-        # var_mean = np.square(variational_posterior.mean).sum()
-        # var_S = (variational_posterior.variance - np.log(variational_posterior.variance)).sum()
-        # return 0.5 * (var_mean + var_S) - 0.5 * variational_posterior.input_dim * variational_posterior.num_data
-    
         mu = variational_posterior.mean
         S = variational_posterior.variance
         pi = self.variational_pi
diff --git a/GPy/models/gmm_bayesian_gplvm.py b/GPy/models/gmm_bayesian_gplvm.py
index 88c79c76..c6cdb0d3 100644
--- a/GPy/models/gmm_bayesian_gplvm.py
+++ b/GPy/models/gmm_bayesian_gplvm.py
@@ -61,12 +61,6 @@ class GmmBayesianGPLVM(SparseGP_MPI):
             px_mu[i] = np.zeros(X_variance.shape)
             px_var[i] = np.ones(X_variance.shape)
         
-        # print("Should print")
-        # print(pi)
-        # print(px_mu)
-        # print(px_var)
-        # print(variational_pi)
-        # print("Didnt print")
         self.variational_prior = GmmNormalPrior(px_mu=px_mu, px_var=px_var, pi=pi,
                                 n_component=n_component, variational_pi=variational_pi)