diff --git a/GPy/core/parameterization/parameter_core.py b/GPy/core/parameterization/parameter_core.py
index 3d674142..067960ce 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
+            if not isinstance(c, Transformation):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