diff --git a/GPy/likelihoods/Gaussian.py b/GPy/likelihoods/Gaussian.py
index b76fd42f..9a992ac1 100644
--- a/GPy/likelihoods/Gaussian.py
+++ b/GPy/likelihoods/Gaussian.py
@@ -19,12 +19,12 @@ class Gaussian(likelihood):
             self._std = np.ones((1,D))
             self.Y = self.data
 
-        # if self.D > self.N:
-        self.YYT = np.dot(self.Y,self.Y.T)
-        self.trYYT = np.trace(self.YYT)
-        # else:
-        #     self.YYT = None
-        #     self.trYYT = None
+        if D > self.N:
+            self.YYT = np.dot(self.Y,self.Y.T)
+            self.trYYT = np.trace(self.YYT)
+        else:
+            self.YYT = None
+            self.trYYT = None
 
         self._set_params(np.asarray(variance))
 
diff --git a/GPy/models/sparse_GP.py b/GPy/models/sparse_GP.py
index 3239d462..b822441e 100644
--- a/GPy/models/sparse_GP.py
+++ b/GPy/models/sparse_GP.py
@@ -49,6 +49,9 @@ class sparse_GP(GP):
             self.has_uncertain_inputs=True
             self.X_uncertainty = X_uncertainty
 
+        if not self.likelihood.is_heteroscedastic:
+            self.likelihood.trYYT = np.trace(np.dot(self.likelihood.Y, self.likelihood.Y.T)) # TODO: something more elegant here?
+
         GP.__init__(self, X, likelihood, kernel=kernel, normalize_X=normalize_X, Xslices=Xslices)
 
         #normalise X uncertainty also