Add tests for MvGaussianRandomWalk with RV params

Added tests for both chol and cov to be random variables.

Author: awray3

pymc3/tests/test_distributions_random.py

@@ -1734,3 +1734,39 @@ def test_with_np_arrays(self, sample_shape, dist_shape, mu_shape, param):
         )
         output_shape = to_tuple(sample_shape) + dist_shape
         assert dist.random(size=sample_shape).shape == output_shape
+
+    @pytest.mark.xfail
+    @pytest.mark.parametrize(
+        ["sample_shape", "dist_shape", "mu_shape"],
+        generate_shapes(include_params=False),
+        ids=str,
+    )
+    def test_with_chol_rv(self, sample_shape, dist_shape, mu_shape):
+        with pm.Model() as model:
+            mu = pm.Normal("mu", 0.0, 1.0, shape=mu_shape)
+            sd_dist = pm.Exponential.dist(1.0, shape=3)
+            chol, corr, stds = pm.LKJCholeskyCov(
+                "chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
+            )
+            mv = pm.MvGaussianRandomWalk("mv", mu, chol=chol, shape=dist_shape)
+            prior = pm.sample_prior_predictive(samples=sample_shape)
+
+        assert prior["mv"].shape == to_tuple(sample_shape) + dist_shape
+
+    @pytest.mark.xfail
+    @pytest.mark.parametrize(
+        ["sample_shape", "dist_shape", "mu_shape"],
+        generate_shapes(include_params=False),
+        ids=str,
+    )
+    def test_with_cov_rv(self, sample_shape, dist_shape, mu_shape):
+        with pm.Model() as model:
+            mu = pm.Normal("mu", 0.0, 1.0, shape=mu_shape)
+            sd_dist = pm.Exponential.dist(1.0, shape=3)
+            chol, corr, stds = pm.LKJCholeskyCov(
+                "chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
+            )
+            mv = pm.MvGaussianRandomWalk("mv", mu, cov=pm.math.dot(chol, chol.T), shape=dist_shape)
+            prior = pm.sample_prior_predictive(samples=sample_shape)
+
+        assert prior["mv"].shape == to_tuple(sample_shape) + dist_shape