Add MvNormal moment

pymc/distributions/multivariate.py

@@ -228,6 +228,13 @@ def dist(cls, mu, cov=None, tau=None, chol=None, lower=True, **kwargs):
         cov = quaddist_matrix(cov, chol, tau, lower)
         return super().dist([mu, cov], **kwargs)
 
+    def get_moment(rv, size, mu, cov):
+        moment = mu
+        if not rv_size_is_none(size):
+            moment_size = at.concatenate([size, mu.shape])
+            moment = at.full(moment_size, mu)
+        return moment
+
     def logp(value, mu, cov):
         """
         Calculate log-probability of Multivariate Normal distribution

pymc/tests/test_distributions_moments.py

@@ -44,6 +44,7 @@
     ZeroInflatedBinomial,
     ZeroInflatedPoisson,
 )
+from pymc.distributions.multivariate import MvNormal
 from pymc.distributions.shape_utils import rv_size_is_none
 from pymc.initial_point import make_initial_point_fn
 from pymc.model import Model
@@ -751,6 +752,40 @@ def test_categorical_moment(p, size, expected):
     assert_moment_is_expected(model, expected)
 
 
+@pytest.mark.parametrize(
+    "mu, cov, size, expected",
+    [
+        (np.ones(1), np.identity(1), None, np.ones(1)),
+        (np.ones(3), np.identity(3), None, np.ones(3)),
+        (np.ones((2, 2)), np.identity(2), None, np.ones((2, 2))),
+        (np.array([1, 0, 3.0]), np.identity(3), None, np.array([1, 0, 3.0])),
+        (np.array([1, 0, 3.0]), np.identity(3), (4, 2), np.full((4, 2, 3), [1, 0, 3.0])),
+        (
+            np.array([1, 3.0]),
+            np.identity(2),
+            5,
+            np.full((5, 2), [1, 3.0]),
+        ),
+        (
+            np.array([1, 3.0]),
+            np.array([[1.0, 0.5], [0.5, 2]]),
+            (4, 5),
+            np.full((4, 5, 2), [1, 3.0]),
+        ),
+        (
+            np.array([[3.0, 5], [1, 4]]),
+            np.identity(2),
+            (4, 5),
+            np.full((4, 5, 2, 2), [[3.0, 5], [1, 4]]),
+        ),
+    ],
+)
+def test_mv_normal_moment(mu, cov, size, expected):
+    with Model() as model:
+        MvNormal("x", mu=mu, cov=cov, size=size)
+    assert_moment_is_expected(model, expected)
+
+
 @pytest.mark.parametrize(
     "mu, sigma, size, expected",
     [