Add Multinomial moments

pymc/distributions/multivariate.py

@@ -525,6 +525,21 @@ def dist(cls, n, p, *args, **kwargs):
 
         return super().dist([n, p], *args, **kwargs)
 
+    def get_moment(rv, size, n, p):
+        if p.ndim > 1:
+            n = at.shape_padright(n)
+        if (p.ndim == 1) & (n.ndim > 0):
+            n = at.shape_padright(n)
+            p = at.shape_padleft(p)
+        mode = at.round(n * p)
+        diff = n - at.sum(mode, axis=-1, keepdims=True)
+        inc_bool_arr = at.abs_(diff) > 0
+        mode = at.inc_subtensor(mode[inc_bool_arr.nonzero()], diff[inc_bool_arr.nonzero()])
+        if not rv_size_is_none(size):
+            output_size = at.concatenate([size, p.shape])
+            mode = at.full(output_size, mode)
+        return mode
+
     def logp(value, n, p):
         """
         Calculate log-probability of Multinomial distribution

pymc/tests/test_distributions.py

@@ -2160,25 +2160,6 @@ def test_multinomial(self, n):
             Multinomial, Vector(Nat, n), {"p": Simplex(n), "n": Nat}, multinomial_logpdf
         )
 
-    @pytest.mark.skip(reason="Moment calculations have not been refactored yet")
-    @pytest.mark.parametrize(
-        "p,n",
-        [
-            [[0.25, 0.25, 0.25, 0.25], 1],
-            [[0.3, 0.6, 0.05, 0.05], 2],
-            [[0.3, 0.6, 0.05, 0.05], 10],
-        ],
-    )
-    def test_multinomial_mode(self, p, n):
-        _p = np.array(p)
-        with Model() as model:
-            m = Multinomial("m", n, _p, _p.shape)
-        assert_allclose(m.distribution.mode.eval().sum(), n)
-        _p = np.array([p, p])
-        with Model() as model:
-            m = Multinomial("m", n, _p, _p.shape)
-        assert_allclose(m.distribution.mode.eval().sum(axis=-1), n)
-
     @pytest.mark.parametrize(
         "p, size, n",
         [
@@ -2206,14 +2187,6 @@ def test_multinomial_random(self, p, size, n):
 
         assert m.eval().shape == size + p.shape
 
-    @pytest.mark.skip(reason="Moment calculations have not been refactored yet")
-    def test_multinomial_mode_with_shape(self):
-        n = [1, 10]
-        p = np.asarray([[0.25, 0.25, 0.25, 0.25], [0.26, 0.26, 0.26, 0.22]])
-        with Model() as model:
-            m = Multinomial("m", n=n, p=p, size=(2, 4))
-        assert_allclose(m.distribution.mode.eval().sum(axis=-1), n)
-
     def test_multinomial_vec(self):
         vals = np.array([[2, 4, 4], [3, 3, 4]])
         p = np.array([0.2, 0.3, 0.5])

pymc/tests/test_distributions_moments.py

@@ -41,6 +41,7 @@
     LogNormal,
     MatrixNormal,
     Moyal,
+    Multinomial,
     MvStudentT,
     NegativeBinomial,
     Normal,
@@ -1104,6 +1105,44 @@ def test_polyagamma_moment(h, z, size, expected):
     assert_moment_is_expected(model, expected)
 
 
+@pytest.mark.parametrize(
+    "p, n, size, expected",
+    [
+        (np.array([0.25, 0.25, 0.25, 0.25]), 1, None, np.array([1, 0, 0, 0])),
+        (np.array([0.3, 0.6, 0.05, 0.05]), 2, None, np.array([1, 1, 0, 0])),
+        (np.array([0.3, 0.6, 0.05, 0.05]), 10, None, np.array([4, 6, 0, 0])),
+        (
+            np.array([[0.3, 0.6, 0.05, 0.05], [0.25, 0.25, 0.25, 0.25]]),
+            10,
+            None,
+            np.array([[4, 6, 0, 0], [4, 2, 2, 2]]),
+        ),
+        (
+            np.array([[0.25, 0.25, 0.25, 0.25], [0.26, 0.26, 0.26, 0.22]]),
+            np.array([1, 10]),
+            None,
+            np.array([[1, 0, 0, 0], [2, 3, 3, 2]]),
+        ),
+        (
+            np.array([0.26, 0.26, 0.26, 0.22]),
+            np.array([1, 10]),
+            None,
+            np.array([[1, 0, 0, 0], [2, 3, 3, 2]]),
+        ),
+        (
+            np.array([[0.25, 0.25, 0.25, 0.25], [0.26, 0.26, 0.26, 0.22]]),
+            np.array([1, 10]),
+            2,
+            np.full((2, 2, 4), [[1, 0, 0, 0], [2, 3, 3, 2]]),
+        ),
+    ],
+)
+def test_multinomial_moment(p, n, size, expected):
+    with Model() as model:
+        Multinomial("x", n=n, p=p, size=size)
+    assert_moment_is_expected(model, expected)
+
+
 @pytest.mark.parametrize(
     "psi, mu, alpha, size, expected",
     [