diff --git a/GPy/core/parameterization/param.py b/GPy/core/parameterization/param.py
index 583c6425..772cbd42 100644
--- a/GPy/core/parameterization/param.py
+++ b/GPy/core/parameterization/param.py
@@ -157,14 +157,14 @@ class Param(ObservableArray, Constrainable, Gradcheckable):
     #===========================================================================
     def tie_to(self, param):
         """
-        :param param: the parameter object to tie this parameter to. 
+        :param param: the parameter object to tie this parameter to.
                       Can be ParamConcatenation (retrieved by regexp search)
-        
+
         Tie this parameter to the given parameter.
         Broadcasting is not allowed, but you can tie a whole dimension to
         one parameter:  self[:,0].tie_to(other), where other is a one-value
         parameter.
-        
+
         Note: For now only one parameter can have ties, so all of a parameter
               will be removed, when re-tieing!
         """
@@ -534,7 +534,7 @@ class ParamConcatenation(object):
     def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
         return self.params[0]._highest_parent_._checkgrad(self, verbose, step, tolerance)
     #checkgrad.__doc__ = Gradcheckable.checkgrad.__doc__
-    
+
     __lt__ = lambda self, val: self._vals() < val
     __le__ = lambda self, val: self._vals() <= val
     __eq__ = lambda self, val: self._vals() == val
diff --git a/GPy/inference/latent_function_inference/laplace.py b/GPy/inference/latent_function_inference/laplace.py
index bc81a86a..4edb9a1d 100644
--- a/GPy/inference/latent_function_inference/laplace.py
+++ b/GPy/inference/latent_function_inference/laplace.py
@@ -92,12 +92,11 @@ class LaplaceInference(object):
         iteration = 0
         while difference > self._mode_finding_tolerance and iteration < self._mode_finding_max_iter:
             W = -likelihood.d2logpdf_df2(f, Y, extra_data=Y_metadata)
-
-            W_f = W*f
             grad = likelihood.dlogpdf_df(f, Y, extra_data=Y_metadata)
 
+            W_f = W*f
+
             b = W_f + grad # R+W p46 line 6.
-            #W12BiW12Kb, B_logdet = self._compute_B_statistics(K, W.copy(), np.dot(K, b), likelihood.log_concave)
             W12BiW12, _, _ = self._compute_B_statistics(K, W, likelihood.log_concave)
             W12BiW12Kb = np.dot(W12BiW12, np.dot(K, b))
 
diff --git a/GPy/testing/likelihood_tests.py b/GPy/testing/likelihood_tests.py
index 7f48ac95..9920d648 100644
--- a/GPy/testing/likelihood_tests.py
+++ b/GPy/testing/likelihood_tests.py
@@ -1,10 +1,10 @@
 import numpy as np
 import unittest
 import GPy
-from GPy.models import GradientChecker
+from ..models import GradientChecker
 import functools
 import inspect
-from GPy.likelihoods import link_functions
+from ..likelihoods import link_functions
 from ..core.parameterization import Param
 from functools import partial
 #np.random.seed(300)