diff --git a/GPy/inference/latent_function_inference/expectation_propagation_dtc.py b/GPy/inference/latent_function_inference/expectation_propagation_dtc.py
index 3625a5bf..be1efd3a 100644
--- a/GPy/inference/latent_function_inference/expectation_propagation_dtc.py
+++ b/GPy/inference/latent_function_inference/expectation_propagation_dtc.py
@@ -5,7 +5,13 @@ from posterior import Posterior
 log_2_pi = np.log(2*np.pi)
 
 class EPDTC(EP):
-    #def __init__(self, epsilon=1e-6, eta=1., delta=1.):
+    def __init__(self, epsilon=1e-6, eta=1., delta=1.):
+        self.epsilon, self.eta, self.delta = epsilon, eta, delta
+        self.reset()
+
+    def reset(self):
+        self.old_mutilde, self.old_vtilde = None, None
+        self._ep_approximation = None
 
     def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None):
         num_data, output_dim = X.shape
@@ -20,8 +26,10 @@ class EPDTC(EP):
         KmmiKmn = np.dot(Kmmi,Kmn)
         K = np.dot(Kmn.T,KmmiKmn)
 
-
-        mu, Sigma, mu_tilde, tau_tilde, Z_hat = self.expectation_propagation(Kmm, Kmn, Y, likelihood, Y_metadata)
+        if self._ep_approximation is None:
+            mu, Sigma, mu_tilde, tau_tilde, Z_hat = self._ep_approximation = self.expectation_propagation(Kmm, Kmn, Y, likelihood, Y_metadata)
+        else:
+            mu, Sigma, mu_tilde, tau_tilde, Z_hat = self._ep_approximation
 
         Wi, LW, LWi, W_logdet = pdinv(K + np.diag(1./tau_tilde))