diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index 254549e6..8ce3482c 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -15,7 +15,7 @@ from parameterization.variational import VariationalPosterior
 from scipy.sparse.base import issparse
 
 import logging
-from GPy.util.normalizer import GaussianNorm
+from GPy.util.normalizer import MeanNorm
 logger = logging.getLogger("GP")
 
 class GP(Model):
@@ -31,7 +31,7 @@ class GP(Model):
     :param Norm normalizer:
         normalize the outputs Y.
         Prediction will be un-normalized using this normalizer.
-        If normalizer is None, we will normalize using GaussianNorm.
+        If normalizer is None, we will normalize using MeanNorm.
         If normalizer is False, no normalization will be done.
 
     .. Note:: Multiple independent outputs are allowed using columns of Y
@@ -52,7 +52,7 @@ class GP(Model):
         logger.info("initializing Y")
 
         if normalizer is None:
-            self.normalizer = GaussianNorm()
+            self.normalizer = MeanNorm()
         elif normalizer is False:
             self.normalizer = None
         else:
diff --git a/GPy/util/normalizer.py b/GPy/util/normalizer.py
index 86ffac86..854726c4 100644
--- a/GPy/util/normalizer.py
+++ b/GPy/util/normalizer.py
@@ -24,25 +24,22 @@ class Norm(object):
         Project the normalized object X into space of Y
         """
         raise NotImplementedError
+    def inverse_variance(self, var):
+        return var
     def scaled(self):
         """
         Whether this Norm object has been initialized.
         """
         raise NotImplementedError
-class GaussianNorm(Norm):
+class MeanNorm(Norm):
     def __init__(self):
         self.mean = None
-        self.std = None
     def scale_by(self, Y):
         Y = np.ma.masked_invalid(Y, copy=False)
         self.mean = Y.mean(0).view(np.ndarray)
-        self.std = Y.std(0).view(np.ndarray)
-        self.std[self.std==0] = 1.
     def normalize(self, Y):
-        return ((Y-self.mean)/self.std)
+        return Y-self.mean
     def inverse_mean(self, X):
-        return ((X*self.std)+self.mean)
-    def inverse_variance(self, var):
-        return (var*self.std**2)
+        return X+self.mean
     def scaled(self):
-        return self.mean is not None and self.std is not None
+        return self.mean is not None