diff --git a/GPy/core/parameterization/index_operations.py b/GPy/core/parameterization/index_operations.py
index 5c3e350f..3bddd899 100644
--- a/GPy/core/parameterization/index_operations.py
+++ b/GPy/core/parameterization/index_operations.py
@@ -109,7 +109,7 @@ class ParameterIndexOperations(object):
         try:
             return self._properties.itervalues()
         except AttributeError:
-	#Changed this from itervalues to values for Py3 compatibility. It didn't break the test suite.
+            #Changed this from itervalues to values for Py3 compatibility. It didn't break the test suite.
             return self._properties.values()
 
     def indices(self):
diff --git a/GPy/core/parameterization/parameter_core.py b/GPy/core/parameterization/parameter_core.py
index 3d674142..bd5c91e9 100644
--- a/GPy/core/parameterization/parameter_core.py
+++ b/GPy/core/parameterization/parameter_core.py
@@ -433,35 +433,34 @@ class Indexable(Nameable, Updateable):
         if self.priors.size == 0:
             return 0.
         x = self.param_array
-	#evaluate the prior log densities
+        #evaluate the prior log densities
         log_p = reduce(lambda a, b: a + b, (p.lnpdf(x[ind]).sum() for p, ind in self.priors.items()), 0)
 
-	#account for the transformation by evaluating the log Jacobian (where things are transformed)
-	log_j = 0.
-	priored_indexes = np.hstack([i for p, i in self.priors.items()])
+        #account for the transformation by evaluating the log Jacobian (where things are transformed)
+        log_j = 0.
+        priored_indexes = np.hstack([i for p, i in self.priors.items()])
         for c,j in self.constraints.items():
             if c is 'fixed':continue
             for jj in j:
                 if jj in priored_indexes:
                     log_j += c.log_jacobian(x[jj])
-	return log_p + log_j
+        return log_p + log_j
 
-	    
     def _log_prior_gradients(self):
         """evaluate the gradients of the priors"""
         if self.priors.size == 0:
             return 0.
         x = self.param_array
         ret = np.zeros(x.size)
-	#compute derivate of prior density
+        #compute derivate of prior density
         [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]
-	#add in jacobian derivatives if transformed
-	priored_indexes = np.hstack([i for p, i in self.priors.items()])
+        #add in jacobian derivatives if transformed
+        priored_indexes = np.hstack([i for p, i in self.priors.items()])
         for c,j in self.constraints.items():
             for jj in j:
                 if jj in priored_indexes:
                     ret[jj] += c.log_jacobian_grad(x[jj])
-	return ret
+        return ret
 
     #===========================================================================
     # Tie parameters together