diff --git a/GPy/testing/bgplvm_minibatch_tests.py b/GPy/testing/bgplvm_minibatch_tests.py
index c5be8b0c..4a824368 100644
--- a/GPy/testing/bgplvm_minibatch_tests.py
+++ b/GPy/testing/bgplvm_minibatch_tests.py
@@ -28,6 +28,29 @@ class BGPLVMTest(unittest.TestCase):
         np.testing.assert_allclose(m.gradient, self.m_full.gradient)
         assert(m.checkgrad())
 
+    def test_predict_missing_data(self):
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=True, stochastic=True, batchsize=self.Y.shape[1])
+        m[:] = self.m_full[:]
+        np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
+        np.testing.assert_allclose(m.gradient, self.m_full.gradient)
+
+        self.assertRaises(NotImplementedError, m.predict, m.X, full_cov=True)
+
+        mu1, var1 = m.predict(m.X, full_cov=False)
+        mu2, var2 = self.m_full.predict(self.m_full.X, full_cov=False)
+        np.testing.assert_allclose(mu1, mu2)
+        np.testing.assert_allclose(var1, var2)
+
+        mu1, var1 = m.predict(m.X.mean, full_cov=True)
+        mu2, var2 = self.m_full.predict(self.m_full.X.mean, full_cov=True)
+        np.testing.assert_allclose(mu1, mu2)
+        np.testing.assert_allclose(var1[:,:,0], var2)
+
+        mu1, var1 = m.predict(m.X.mean, full_cov=False)
+        mu2, var2 = self.m_full.predict(self.m_full.X.mean, full_cov=False)
+        np.testing.assert_allclose(mu1, mu2)
+        np.testing.assert_allclose(var1[:,[0]], var2)
+
     def test_lik_comparisons_m0_s0(self):
         # Test if the different implementations give the exact same likelihood as the full model.
         # All of the following settings should give the same likelihood and gradients as the full model:
@@ -71,6 +94,16 @@ class BGPLVMTest(unittest.TestCase):
         m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=False, stochastic=True, batchsize=4)
         assert(m.checkgrad())
 
+    def test_predict(self):
+        # Test if the different implementations give the exact same likelihood as the full model.
+        # All of the following settings should give the same likelihood and gradients as the full model:
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=True, stochastic=True, batchsize=self.Y.shape[1])
+        m[:] = self.m_full[:]
+        np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
+        np.testing.assert_allclose(m.gradient, self.m_full.gradient)
+        assert(m.checkgrad())
+
+
 if __name__ == "__main__":
     #import sys;sys.argv = ['', 'Test.testName']
     unittest.main()
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