diff --git a/GPy/testing/rv_transformation_tests.py b/GPy/testing/rv_transformation_tests.py
index 34e54e57..18dccd36 100644
--- a/GPy/testing/rv_transformation_tests.py
+++ b/GPy/testing/rv_transformation_tests.py
@@ -14,8 +14,8 @@ class TestModel(GPy.core.Model):
     """
     A simple GPy model with one parameter.
     """
-    def __init__(self, name):
-        GPy.core.Model.__init__(self, name)
+    def __init__(self):
+        GPy.core.Model.__init__(self, 'test_model')
         theta = GPy.core.Param('theta', 1.)
         self.link_parameter(theta)
 
@@ -26,7 +26,7 @@ class TestModel(GPy.core.Model):
 class RVTransformationTestCase(unittest.TestCase):
 
     def _test_trans(self, trans):
-        m = TestModel(trans.__class__.__name__)
+        m = TestModel()
         prior = GPy.priors.LogGaussian(.5, 0.1)
         m.theta.set_prior(prior)
         m.theta.unconstrain()
@@ -34,7 +34,7 @@ class RVTransformationTestCase(unittest.TestCase):
         # The PDF of the transformed variables
         p_phi = lambda phi : np.exp(-m._objective_grads(phi)[0])
         # To the empirical PDF of:
-        theta_s = prior.rvs(100000)
+        theta_s = prior.rvs(1e6)
         phi_s = trans.finv(theta_s)
         # which is essentially a kernel density estimation
         kde = st.gaussian_kde(phi_s)
@@ -56,15 +56,12 @@ class RVTransformationTestCase(unittest.TestCase):
         # The following test cannot be very accurate
         self.assertTrue(np.linalg.norm(pdf_phi - kde(phi)) / np.linalg.norm(kde(phi)) <= 1e-1)
         # Check the gradients at a few random points
-        print(theta_s.min(), theta_s.max())
         for i in range(5):
-            print(theta_s[i])
             m.theta = theta_s[i]
             self.assertTrue(m.checkgrad(verbose=True))
 
     def test_Logexp(self):
         self._test_trans(GPy.constraints.Logexp())
-    def test_Exponent(self):
         self._test_trans(GPy.constraints.Exponent())