diff --git a/bilby/core/prior.py b/bilby/core/prior.py
index c03aa80338f670c5f8b647d7a098bfa5f60dc1ba..4e731629ad51b3a6d70fa12a079dfd02363ca025 100644
--- a/bilby/core/prior.py
+++ b/bilby/core/prior.py
@@ -2223,7 +2223,7 @@ class MultivariateGaussianDist(object):
         probability will not be properly normalised.
         """
 
-        return np.exp(self.log_prob(samp))
+        return np.exp(self.ln_prob(samp))
 
     def __len__(self):
         return len(self.names)
diff --git a/test/prior_test.py b/test/prior_test.py
index 28df11dc9216dcdcf6d29ad0745e41d4a8bdefee..e3abc3e5ee3b34cb0a0fc41ea33e2258c2447f1f 100644
--- a/test/prior_test.py
+++ b/test/prior_test.py
@@ -129,6 +129,10 @@ class TestPriorClasses(unittest.TestCase):
                                                         mus=[1, 1],
                                                         covs=np.array([[2., 0.5], [0.5, 2.]]),
                                                         weights=1.)
+        mvn = bilby.core.prior.MultivariateGaussianDist(names=['testa', 'testb'],
+                                                        mus=[1, 1],
+                                                        covs=np.array([[2., 0.5], [0.5, 2.]]),
+                                                        weights=1.)
 
         self.priors = [
             bilby.core.prior.DeltaFunction(name='test', unit='unit', peak=1),
@@ -162,6 +166,8 @@ class TestPriorClasses(unittest.TestCase):
             bilby.gw.prior.AlignedSpin(name='test', unit='unit'),
             bilby.core.prior.MultivariateGaussian(mvg=mvg, name='testa', unit='unit'),
             bilby.core.prior.MultivariateGaussian(mvg=mvg, name='testb', unit='unit'),
+            bilby.core.prior.MultivariateNormal(mvg=mvn, name='testa', unit='unit'),
+            bilby.core.prior.MultivariateNormal(mvg=mvn, name='testb', unit='unit')
         ]
 
     def test_minimum_rescaling(self):
@@ -447,7 +453,7 @@ class TestPriorClasses(unittest.TestCase):
                     bilby.core.prior.HalfGaussian, bilby.core.prior.LogNormal,
                     bilby.core.prior.Exponential, bilby.core.prior.StudentT,
                     bilby.core.prior.Logistic, bilby.core.prior.Cauchy,
-                    bilby.core.prior.Gamma)):
+                    bilby.core.prior.Gamma, bilby.core.prior.MultivariateGaussian)):
                 continue
             prior.maximum = (prior.maximum + prior.minimum) / 2
             self.assertTrue(max(prior.sample(10000)) < prior.maximum)
@@ -459,7 +465,7 @@ class TestPriorClasses(unittest.TestCase):
                     bilby.core.prior.HalfGaussian, bilby.core.prior.LogNormal,
                     bilby.core.prior.Exponential, bilby.core.prior.StudentT,
                     bilby.core.prior.Logistic, bilby.core.prior.Cauchy,
-                    bilby.core.prior.Gamma)):
+                    bilby.core.prior.Gamma, bilby.core.prior.MultivariateGaussian)):
                 continue
             prior.minimum = (prior.maximum + prior.minimum) / 2
             self.assertTrue(min(prior.sample(10000)) > prior.minimum)
diff --git a/test/sampler_test.py b/test/sampler_test.py
index 2500fab13aede95032c78cda76e8a7832a7f2dd5..f3063b4628e9d76b3808a557258ea8a3321cc7eb 100644
--- a/test/sampler_test.py
+++ b/test/sampler_test.py
@@ -363,7 +363,7 @@ class TestPyMC3(unittest.TestCase):
             draws=500, step=None, init='auto', n_init=200000, start=None, trace=None, chain_idx=0,
             chains=2, cores=1, tune=500, nuts_kwargs=None, step_kwargs=None, progressbar=True,
             model=None, random_seed=None, live_plot=False, discard_tuned_samples=True,
-            live_plot_kwargs=None, compute_convergence_checks=True, use_mmap=False)
+            live_plot_kwargs=None, compute_convergence_checks=True)
         self.assertDictEqual(expected, self.sampler.kwargs)
 
     def test_translate_kwargs(self):
@@ -371,7 +371,7 @@ class TestPyMC3(unittest.TestCase):
             draws=500, step=None, init='auto', n_init=200000, start=None, trace=None, chain_idx=0,
             chains=2, cores=1, tune=500, nuts_kwargs=None, step_kwargs=None, progressbar=True,
             model=None, random_seed=None, live_plot=False, discard_tuned_samples=True,
-            live_plot_kwargs=None, compute_convergence_checks=True, use_mmap=False)
+            live_plot_kwargs=None, compute_convergence_checks=True)
         self.sampler.kwargs['draws'] = 123
         for equiv in bilby.core.sampler.base_sampler.NestedSampler.npoints_equiv_kwargs:
             new_kwargs = self.sampler.kwargs.copy()