diff --git a/GPy/core/parameterization/parameter_core.py b/GPy/core/parameterization/parameter_core.py
index 115acf3a..fb6b3a8c 100644
--- a/GPy/core/parameterization/parameter_core.py
+++ b/GPy/core/parameterization/parameter_core.py
@@ -518,7 +518,7 @@ class Indexable(Nameable, Observable):
             self.constrain_negative(warning)
         elif prior.domain is _REAL:
             rav_i = self._raveled_index()
-            assert all(all(c.domain is _REAL for c in con) for con in self.constraints.properties_for(rav_i))
+            assert all(all(False if c is __fixed__ else c.domain is _REAL for c in con) for con in self.constraints.properties_for(rav_i)), 'Domain of prior and constraint have to match, please unconstrain if you REALLY wish to use this prior'
 
     def unset_priors(self, *priors):
         """
@@ -824,7 +824,7 @@ class OptimizationHandlable(Indexable):
     #===========================================================================
     # Randomizeable
     #===========================================================================
-    def randomize(self, rand_gen=np.random.normal, *args, **kwargs):
+    def randomize(self, rand_gen=None, *args, **kwargs):
         """
         Randomize the model.
         Make this draw from the prior if one exists, else draw from given random generator
@@ -834,6 +834,8 @@ class OptimizationHandlable(Indexable):
         :param float scale: scale parameter for random number generator
         :param args, kwargs: will be passed through to random number generator
         """
+        if rand_gen is None:
+            rand_gen = np.random.normal
         # first take care of all parameters (from N(0,1))
         x = rand_gen(size=self._size_transformed(), *args, **kwargs)
         updates = self.update_model()
diff --git a/GPy/testing/prior_tests.py b/GPy/testing/prior_tests.py
index db6cc685..6a61fbb5 100644
--- a/GPy/testing/prior_tests.py
+++ b/GPy/testing/prior_tests.py
@@ -45,6 +45,69 @@ class PriorTests(unittest.TestCase):
         # should raise an assertionerror.
         self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
 
+    def test_set_prior(self):
+        xmin, xmax = 1, 2.5*np.pi
+        b, C, SNR = 1, 0, 0.1
+        X = np.linspace(xmin, xmax, 500)
+        y  = b*X + C + 1*np.sin(X)
+        y += 0.05*np.random.randn(len(X))
+        X, y = X[:, None], y[:, None]
+        m = GPy.models.GPRegression(X, y)
+
+        gaussian = GPy.priors.Gaussian(1, 1)
+        #m.rbf.set_prior(gaussian)
+        # setting a Gaussian prior on non-negative parameters
+        # should raise an assertionerror.
+        self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
+
+    def test_set_gaussian_for_reals(self):
+        xmin, xmax = 1, 2.5*np.pi
+        b, C, SNR = 1, 0, 0.1
+        X = np.linspace(xmin, xmax, 500)
+        y  = b*X + C + 1*np.sin(X)
+        y += 0.05*np.random.randn(len(X))
+        X, y = X[:, None], y[:, None]
+        m = GPy.models.SparseGPRegression(X, y)
+
+        gaussian = GPy.priors.Gaussian(1, 1)
+        m.Z.set_prior(gaussian)
+        # setting a Gaussian prior on non-negative parameters
+        # should raise an assertionerror.
+        #self.assertRaises(AssertionError, m.Z.set_prior, gaussian)
+
+
+
+    def test_fixed_domain_check(self):
+        xmin, xmax = 1, 2.5*np.pi
+        b, C, SNR = 1, 0, 0.1
+        X = np.linspace(xmin, xmax, 500)
+        y  = b*X + C + 1*np.sin(X)
+        y += 0.05*np.random.randn(len(X))
+        X, y = X[:, None], y[:, None]
+        m = GPy.models.GPRegression(X, y)
+
+        m.rbf.fix()
+        gaussian = GPy.priors.Gaussian(1, 1)
+        # setting a Gaussian prior on non-negative parameters
+        # should raise an assertionerror.
+        self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
+
+    def test_fixed_domain_check1(self):
+        xmin, xmax = 1, 2.5*np.pi
+        b, C, SNR = 1, 0, 0.1
+        X = np.linspace(xmin, xmax, 500)
+        y  = b*X + C + 1*np.sin(X)
+        y += 0.05*np.random.randn(len(X))
+        X, y = X[:, None], y[:, None]
+        m = GPy.models.GPRegression(X, y)
+
+        m.kern.lengthscale.fix()
+        gaussian = GPy.priors.Gaussian(1, 1)
+        # setting a Gaussian prior on non-negative parameters
+        # should raise an assertionerror.
+        self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
+
+
 
 if __name__ == "__main__":
     print "Running unit tests, please be (very) patient..."