diff --git a/GPy/core/gp_base.py b/GPy/core/gp_base.py
index 827de434..9466a011 100644
--- a/GPy/core/gp_base.py
+++ b/GPy/core/gp_base.py
@@ -183,11 +183,10 @@ class GPBase(Model):
                 m, v = self._raw_predict(Xgrid, which_parts=which_parts)
                 lower = m - 2*np.sqrt(v)
                 upper = m + 2*np.sqrt(v)
-                Y = self.likelihood.Y
+                Y = self.Y
             else:
-                m, v, lower, upper = self.predict(Xgrid, which_parts=which_parts, sampling=False) #Compute the exact mean
-                m_, v_, lower, upper = self.predict(Xgrid, which_parts=which_parts, sampling=True, num_samples=15000) #Apporximate the percentiles
-                Y = self.likelihood.data
+                m, v, lower, upper = self.predict(Xgrid, which_parts=which_parts) #Compute the exact mean
+                Y = self.Y
             for d in which_data_ycols:
                 gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
                 ax.plot(Xu[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
diff --git a/GPy/inference/latent_function_inference/posterior.py b/GPy/inference/latent_function_inference/posterior.py
index 79db69e5..9ebffff8 100644
--- a/GPy/inference/latent_function_inference/posterior.py
+++ b/GPy/inference/latent_function_inference/posterior.py
@@ -13,7 +13,7 @@ class Posterior(object):
     schemes and the model classes. 
 
     """
-    def __init__(self, log_marginal, dL_dK, dL_dtheta_lik, woodbury_chol, woodbury_vector, K):
+    def __init__(self, log_marginal, dL_dK, dL_dtheta_lik, woodbury_chol=None, woodbury_vector=None, K=None, mean=None, cov=None, K_chol=None):
         """
         log_marginal: log p(Y|X)
         DL_dK: d/dK log p(Y|X)
@@ -21,17 +21,51 @@ class Posterior(object):
         woodbury_chol : a lower triangular matrix L that satisfies posterior_covariance = K - K L^{-T} L^{-1} K
         woodbury_vector : a matrix (or vector, as Nx1 matrix) M which satisfies posterior_mean = K M
         K : the proir covariance (required for lazy computation of various quantities)
+        mean : the posterior mean
+        cov : the posterior covariance
+
+        Not all of the above need to be supplied! You *must* supply:
+          
+          log_marginal
+          dL_dK
+          dL_dtheta_lik
+          K (for lazy computation)
+
+       You may supply either:
+         
+          woodbury_chol
+          woodbury_vector
+
+        Or:
+          
+          mean
+          cov
+          K_chol (for lazy computation)
+
+        From the supplied quantities, all of the others will be computed on demand (lazy computation)
+          
         """
+        #obligatory
         self.log_marginal = log_marginal
         self.dL_dK = dL_dK
         self.dL_dtheta_lik = dL_dtheta_lik
-        self._woodbury_chol = woodbury_chol
-        self._woodbury_vector = woodbury_vector
         self._K = K
 
-        #these are computed lazily below
-        self._mean = None
-        self._covariance = None
+        if ((woodbury_chol is not None) and (woodbury_vector is not None) and (K is not None)) or ((mean is not None) and (cov is not None) and (K is not None)):
+            pass # we have sufficient to compute the posterior
+        else:
+            raise ValueError, "insufficient onformation to compute the posterior"
+
+        #option 1:
+        self._woodbury_chol = woodbury_chol
+        self._woodbury_vector = woodbury_vector
+
+        #option 2:
+        self._mean = mean
+        self._covariance = cov
+        self._K_chol = K_chol
+
+        #copmute this lazily
         self._precision = None
 
     @property
@@ -53,6 +87,20 @@ class Posterior(object):
             self._precision = np.linalg.inv(self.covariance)
         return self._precision
 
+    @property
+    def woodbury_chol(self):
+        if self._woodbury_chol is None:
+            ???
+        else:
+            return self._woodbury_chol
+
+    @property
+    def woodbury_vector(self):
+        if self._woodbury_vector is None:
+            ???
+        else:
+            return self._woodbury_vector
+
 
 
 
diff --git a/GPy/models/gp_regression.py b/GPy/models/gp_regression.py
index 4ebf2e25..a833d11b 100644
--- a/GPy/models/gp_regression.py
+++ b/GPy/models/gp_regression.py
@@ -27,7 +27,7 @@ class GPRegression(GP):
 
         likelihood = likelihoods.Gaussian()
 
-        super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='gp regression')
+        super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='gp_regression')
 
     def getstate(self):
         return GP.getstate(self)