diff --git a/GPy/inference/latent_function_inference/exact_gaussian_inference.py b/GPy/inference/latent_function_inference/exact_gaussian_inference.py
index 74f66fe6..a5519774 100644
--- a/GPy/inference/latent_function_inference/exact_gaussian_inference.py
+++ b/GPy/inference/latent_function_inference/exact_gaussian_inference.py
@@ -21,7 +21,7 @@ class ExactGaussianInference(LatentFunctionInference):
     def __init__(self):
         pass#self._YYTfactor_cache = caching.cache()
 
-    def inference(self, kern, X, likelihood, Y, mean_function=None, Y_metadata=None, K=None, precision=None, Z_tilde=None):
+    def inference(self, kern, X, likelihood, Y, mean_function=None, Y_metadata=None, K=None, variance=None, Z_tilde=None):
         """
         Returns a Posterior class containing essential quantities of the posterior
         """
@@ -31,8 +31,8 @@ class ExactGaussianInference(LatentFunctionInference):
         else:
             m = mean_function.f(X)
 
-        if precision is None:
-            precision = likelihood.gaussian_variance(Y_metadata)
+        if variance is None:
+            variance = likelihood.gaussian_variance(Y_metadata)
 
         YYT_factor = Y-m
 
@@ -40,7 +40,7 @@ class ExactGaussianInference(LatentFunctionInference):
             K = kern.K(X)
 
         Ky = K.copy()
-        diag.add(Ky, precision+1e-8)
+        diag.add(Ky, variance+1e-8)
 
         Wi, LW, LWi, W_logdet = pdinv(Ky)
 
diff --git a/GPy/inference/latent_function_inference/expectation_propagation.py b/GPy/inference/latent_function_inference/expectation_propagation.py
index 077c9e20..01560b3c 100644
--- a/GPy/inference/latent_function_inference/expectation_propagation.py
+++ b/GPy/inference/latent_function_inference/expectation_propagation.py
@@ -66,7 +66,7 @@ class EP(EPBase, ExactGaussianInference):
             #if we've already run EP, just use the existing approximation stored in self._ep_approximation
             mu, Sigma, mu_tilde, tau_tilde, Z_tilde = self._ep_approximation
 
-        return super(EP, self).inference(kern, X, likelihood, mu_tilde[:,None], mean_function=mean_function, Y_metadata=Y_metadata, precision=1./tau_tilde, K=K, Z_tilde=np.log(Z_tilde).sum())
+        return super(EP, self).inference(kern, X, likelihood, mu_tilde[:,None], mean_function=mean_function, Y_metadata=Y_metadata, variance=1./tau_tilde, K=K, Z_tilde=np.log(Z_tilde).sum())
 
     def expectation_propagation(self, K, Y, likelihood, Y_metadata):