diff --git a/GPy/inference/latent_function_inference/var_dtc.py b/GPy/inference/latent_function_inference/var_dtc.py
index 64ee30c4..70637e3b 100644
--- a/GPy/inference/latent_function_inference/var_dtc.py
+++ b/GPy/inference/latent_function_inference/var_dtc.py
@@ -246,12 +246,10 @@ class VarDTCMissingData(LatentFunctionInference):
             uncertain_inputs = True
             psi0_all = kern.psi0(Z, X)
             psi1_all = kern.psi1(Z, X)
-            psi2_all = kern.psi2(Z, X)
         else:
             uncertain_inputs = False
             psi0_all = kern.Kdiag(X)
             psi1_all = kern.K(X, Z)
-            psi2_all = None
 
         Ys, traces = self._Y(Y)
         beta_all = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6)
@@ -262,7 +260,7 @@ class VarDTCMissingData(LatentFunctionInference):
         dL_dpsi0_all = np.zeros(Y.shape[0])
         dL_dpsi1_all = np.zeros((Y.shape[0], num_inducing))
         if uncertain_inputs:
-            dL_dpsi2_all = np.zeros((Y.shape[0], num_inducing, num_inducing))
+            dL_dpsi2_all = np.zeros((num_inducing, num_inducing))
 
         dL_dR = 0
         woodbury_vector = np.zeros((num_inducing, Y.shape[1]))
@@ -278,6 +276,7 @@ class VarDTCMissingData(LatentFunctionInference):
 
         size = Y.shape[1]
         next_ten = 0
+
         for i, [y, v, trYYT] in enumerate(itertools.izip(Ys, self._inan.T, traces)):
             if ((i+1.)/size) >= next_ten:
                 logger.info('inference {:> 6.1%}'.format((i+1.)/size))
@@ -290,13 +289,13 @@ class VarDTCMissingData(LatentFunctionInference):
 
             psi0 = psi0_all[v]
             psi1 = psi1_all[v, :]
-            if uncertain_inputs: psi2 = psi2_all[v, :]
+            if uncertain_inputs: psi2 = kern.psi2(Z, X[v, :])
             else: psi2 = None
             num_data = psi1.shape[0]
 
             if uncertain_inputs:
                 if het_noise: psi2_beta = psi2 * (beta.flatten().reshape(num_data, 1, 1)).sum(0)
-                else: psi2_beta = psi2.sum(0) * beta
+                else: psi2_beta = psi2 * beta
                 A = LmInv.dot(psi2_beta.dot(LmInv.T))
             else:
                 if het_noise: tmp = psi1 * (np.sqrt(beta.reshape(num_data, 1)))
@@ -331,7 +330,7 @@ class VarDTCMissingData(LatentFunctionInference):
             dL_dpsi0_all[v] += dL_dpsi0
             dL_dpsi1_all[v, :] += dL_dpsi1
             if uncertain_inputs:
-                dL_dpsi2_all[v, :] += dL_dpsi2
+                dL_dpsi2_all += dL_dpsi2
 
             # log marginal likelihood
             log_marginal += _compute_log_marginal_likelihood(likelihood, num_data, output_dim, beta, het_noise,