diff --git a/GPy/core/model.py b/GPy/core/model.py
index 00c6d5ff..0fdf4800 100644
--- a/GPy/core/model.py
+++ b/GPy/core/model.py
@@ -303,6 +303,7 @@ class Model(Parameterized):
             denominator = (2 * np.dot(dx, gradient))
             global_ratio = (f1 - f2) / np.where(denominator == 0., 1e-32, denominator)
             global_diff = np.abs(f1 - f2) < tolerance and np.allclose(gradient, 0, atol=tolerance)
+            print self.mpi_comm.rank,global_ratio,global_diff
             if global_ratio is np.nan:
                 global_ratio = 0
             return np.abs(1. - global_ratio) < tolerance or global_diff
diff --git a/GPy/core/parameterization/parameter_core.py b/GPy/core/parameterization/parameter_core.py
index 815f069b..82c494b2 100644
--- a/GPy/core/parameterization/parameter_core.py
+++ b/GPy/core/parameterization/parameter_core.py
@@ -13,7 +13,7 @@ Observable Pattern for patameterization
 
 """
 
-from transformations import Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED
+from transformations import Transformation,Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED
 import numpy as np
 import re
 import logging
@@ -541,7 +541,8 @@ class Indexable(Nameable, Observable):
         Constrain the parameter to the given
         :py:class:`GPy.core.transformations.Transformation`.
         """
-        self.param_array[...] = transform.initialize(self.param_array)
+        if isinstance(transform, Transformation):
+            self.param_array[...] = transform.initialize(self.param_array)
         reconstrained = self.unconstrain()
         added = self._add_to_index_operations(self.constraints, reconstrained, transform, warning)
         self.notify_observers(self, None if trigger_parent else -np.inf)
@@ -617,7 +618,7 @@ class Indexable(Nameable, Observable):
         """
         Helper preventing copy code.
         This adds the given what (transformation, prior etc) to parameter index operations which.
-        revonstrained are reconstrained indices.
+        reconstrained are reconstrained indices.
         warn when reconstraining parameters if warning is True.
         TODO: find out which parameters have changed specifically
         """
diff --git a/GPy/core/parameterization/variational.py b/GPy/core/parameterization/variational.py
index 5412a70d..2afeafbb 100644
--- a/GPy/core/parameterization/variational.py
+++ b/GPy/core/parameterization/variational.py
@@ -7,7 +7,7 @@ Created on 6 Nov 2013
 import numpy as np
 from parameterized import Parameterized
 from param import Param
-from transformations import Logexp, Logistic
+from transformations import Logexp, Logistic,__fixed__
 
 class VariationalPrior(Parameterized):
     def __init__(self, name='latent space', **kw):
@@ -35,12 +35,15 @@ class NormalPrior(VariationalPrior):
 
 class SpikeAndSlabPrior(VariationalPrior):
     def __init__(self, pi=None, learnPi=False, variance = 1.0, name='SpikeAndSlabPrior', **kw):
-        super(VariationalPrior, self).__init__(name=name, **kw)
-        self.pi = Param('pi', pi, Logistic(1e-10,1.-1e-10))
+        super(SpikeAndSlabPrior, self).__init__(name=name, **kw)        
         self.variance = Param('variance',variance)
         self.learnPi = learnPi
         if learnPi:
-            self.add_parameters(self.pi)
+            self.pi = Param('Pi', pi, Logistic(1e-10,1.-1e-10))
+        else:
+            self.pi = Param('Pi', pi, __fixed__)
+        self.add_parameter(self.pi)
+
 
     def KL_divergence(self, variational_posterior):
         mu = variational_posterior.mean