diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py
index 2c0e7547..29daa71d 100644
--- a/GPy/core/sparse_gp.py
+++ b/GPy/core/sparse_gp.py
@@ -68,7 +68,7 @@ class SparseGP(GP):
             self.psi1 = self.kern.K(self.X, self.Z)
             self.psi2 = None
 
-        self.posterior = self.inference_method.inference(??)
+        #self.posterior = self.inference_method.inference(??)
         super(SparseGP, self).parameters_changed()
 
 
diff --git a/GPy/core/svigp.py b/GPy/core/svigp.py
index 95c3b592..13c4ad58 100644
--- a/GPy/core/svigp.py
+++ b/GPy/core/svigp.py
@@ -4,12 +4,12 @@
 import numpy as np
 import pylab as pb
 from ..util.linalg import pdinv, mdot, tdot, dpotrs, dtrtrs, jitchol, backsub_both_sides
-from gp_base import GPBase
+from gp import GP
 import time
 import sys
 
 
-class SVIGP(GPBase):
+class SVIGP(GP):
     """
 
     Stochastic Variational inference in a Gaussian Process
@@ -22,7 +22,7 @@ class SVIGP(GPBase):
 
     Additional kwargs are used as for a sparse GP. They include:
 
-    :param q_u: canonical parameters of the distribution squasehd into a 1D array
+    :param q_u: canonical parameters of the distribution sqehd into a 1D array
     :type q_u: np.ndarray
     :param M: Number of inducing points (optional, default 10. Ignored if Z is not None)
     :type M: int
@@ -44,7 +44,7 @@ class SVIGP(GPBase):
 
 
     def __init__(self, X, likelihood, kernel, Z, q_u=None, batchsize=10, X_variance=None):
-        GPBase.__init__(self, X, likelihood, kernel, normalize_X=False)
+        GP.__init__(self, X, likelihood, kernel, normalize_X=False)
         self.batchsize=batchsize
         self.Y = self.likelihood.Y.copy()
         self.Z = Z
@@ -92,7 +92,7 @@ class SVIGP(GPBase):
 
     def getstate(self):
         steplength_params = [self.hbar_t, self.tau_t, self.gbar_t, self.gbar_t1, self.gbar_t2, self.hbar_tp, self.tau_tp, self.gbar_tp, self.adapt_param_steplength, self.adapt_vb_steplength, self.vb_steplength, self.param_steplength]
-        return GPBase.getstate(self) + \
+        return GP.getstate(self) + \
             [self.get_vb_param(),
              self.Z,
              self.num_inducing,
@@ -139,7 +139,7 @@ class SVIGP(GPBase):
         self.num_inducing = state.pop()
         self.Z = state.pop()
         vb_param = state.pop()
-        GPBase.setstate(self, state)
+        GP.setstate(self, state)
         self.set_vb_param(vb_param)
 
     def _compute_kernel_matrices(self):
@@ -489,7 +489,7 @@ class SVIGP(GPBase):
         #horrible hack here:
         data = self.likelihood.data.copy()
         self.likelihood.data = self.Y
-        GPBase.plot(self, ax=ax, **kwargs)
+        GP.plot(self, ax=ax, **kwargs)
         self.likelihood.data = data
 
         Zu = self.Z * self._Xscale + self._Xoffset