diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py
index 130e56e2..1879145a 100644
--- a/GPy/core/sparse_gp.py
+++ b/GPy/core/sparse_gp.py
@@ -7,6 +7,7 @@ from gp import GP
 from parameterization.param import Param
 from ..inference.latent_function_inference import varDTC
 from .. import likelihoods
+from GPy.util.misc import param_to_array
 
 class SparseGP(GP):
     """
@@ -54,7 +55,10 @@ class SparseGP(GP):
         self.add_parameter(self.Z, index=0)
 
     def parameters_changed(self):
-        self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
+        Xvar = self.X_variance
+        if self.X_variance is not None:
+            Xvar = param_to_array(self.X_variance)
+        self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, param_to_array(self.X), Xvar, param_to_array(self.Z), self.likelihood, self.Y)
 
         #The derivative of the bound wrt the inducing inputs Z
         self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
diff --git a/GPy/inference/latent_function_inference/varDTC.py b/GPy/inference/latent_function_inference/varDTC.py
index e156a273..237ab463 100644
--- a/GPy/inference/latent_function_inference/varDTC.py
+++ b/GPy/inference/latent_function_inference/varDTC.py
@@ -6,6 +6,7 @@ from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dpotri, sy
 import numpy as np
 from ...util.linalg import dtrtri
 from ...util.caching import Cacher
+from ...util.misc import param_to_array
 log_2_pi = np.log(2*np.pi)
 
 class VarDTC(object):
@@ -25,7 +26,7 @@ class VarDTC(object):
         self.get_YYTfactor = Cacher(self._get_YYTfactor, 1)
     
     def _get_trYYT(self, Y):
-        return np.sum(np.square(Y))
+        return param_to_array(np.sum(np.square(Y)))
 
     def _get_YYTfactor(self, Y):
         """
@@ -35,7 +36,7 @@ class VarDTC(object):
         """
         N, D = Y.shape
         if (N>D):
-            return Y
+            return param_to_array(Y)
         else:
             return jitchol(tdot(Y))
             
diff --git a/GPy/testing/psi_stat_gradient_tests.py b/GPy/testing/psi_stat_gradient_tests.py
index 56586d3b..32986c77 100644
--- a/GPy/testing/psi_stat_gradient_tests.py
+++ b/GPy/testing/psi_stat_gradient_tests.py
@@ -71,16 +71,13 @@ class DPsiStatTest(unittest.TestCase):
         for k in self.kernels:
             m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,\
                              num_inducing=self.num_inducing, kernel=k)
-            #m.ensure_default_constraints(warning=0)
             m.randomize()
-            import ipdb;ipdb.set_trace()
             assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k._parameters_)))
         
     def testPsi1(self):
         for k in self.kernels:
             m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
                      num_inducing=self.num_inducing, kernel=k)
-            m.ensure_default_constraints(warning=0)
             m.randomize()
             assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k._parameters_)))
 
@@ -88,35 +85,30 @@ class DPsiStatTest(unittest.TestCase):
         k = self.kernels[0]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
                  num_inducing=self.num_inducing, kernel=k)
-        m.ensure_default_constraints(warning=0)
         m.randomize()
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
     def testPsi2_lin_bia(self):
         k = self.kernels[3]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
                      num_inducing=self.num_inducing, kernel=k)
-        m.ensure_default_constraints(warning=0)
         m.randomize()
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
     def testPsi2_rbf(self):
         k = self.kernels[1]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
                      num_inducing=self.num_inducing, kernel=k)
-        m.ensure_default_constraints(warning=0)
         m.randomize()
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
     def testPsi2_rbf_bia(self):
         k = self.kernels[-1]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
                      num_inducing=self.num_inducing, kernel=k)
-        m.ensure_default_constraints(warning=0)
         m.randomize()
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
     def testPsi2_bia(self):
         k = self.kernels[2]
         m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
                      num_inducing=self.num_inducing, kernel=k)
-        m.ensure_default_constraints(warning=0)
         m.randomize()
         assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
 
diff --git a/GPy/util/misc.py b/GPy/util/misc.py
index f519a1df..dc327324 100644
--- a/GPy/util/misc.py
+++ b/GPy/util/misc.py
@@ -184,4 +184,4 @@ from :class:ndarray)"""
     assert len(param) > 0, "At least one parameter needed"
     if len(param) == 1:
         return param[0].view(np.ndarray)
-    return map(lambda x: x.view(np.ndarray), param)
+    return [x.view(np.ndarray) for x in param]