Adds tests and mode for dirichlet multinomial distribution

pymc/distributions/multivariate.py

@@ -453,9 +453,7 @@ def get_moment(rv, size, a):
         norm_constant = at.sum(a, axis=-1)[..., None]
         moment = a / norm_constant
         if not rv_size_is_none(size):
-            if isinstance(size, int):
-                size = (size,)
-            moment = at.full((*size, *a.shape), moment)
+            moment = at.full(at.concatenate([size, a.shape]), moment)
         return moment
 
     def logp(value, a):
@@ -684,6 +682,19 @@ def dist(cls, n, a, *args, **kwargs):
 
         return super().dist([n, a], **kwargs)
 
+    def get_moment(rv, size, n, a):
+        p = a / at.sum(a, axis=-1)
+        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()])
+        # Reshape mode according to base shape (ignoring size)
+        mode = at.reshape(mode, rv.shape[size.size :])
+        if not rv_size_is_none(size):
+            output_size = at.concatenate([size, mode.shape])
+            mode = at.full(output_size, mode)
+        return mode
+
     def logp(value, n, a):
         """
         Calculate log-probability of DirichletMultinomial distribution

pymc/tests/test_distributions_moments.py

@@ -21,6 +21,7 @@
     Constant,
     DensityDist,
     Dirichlet,
+    DirichletMultinomial,
     DiscreteUniform,
     DiscreteWeibull,
     ExGaussian,
@@ -112,7 +113,6 @@ def test_all_distributions_have_moments():
 
     # Distributions that have been refactored but don't yet have moments
     not_implemented |= {
-        dist_module.multivariate.DirichletMultinomial,
         dist_module.multivariate.Wishart,
     }
 
@@ -797,10 +797,7 @@ def test_discrete_weibull_moment(q, beta, size, expected):
         ),
         (
             np.full(shape=np.array([7, 3]), fill_value=np.array([13, 17, 19])),
-            (
-                11,
-                5,
-            ),
+            (11, 5),
             np.broadcast_to([13, 17, 19], shape=[11, 5, 7, 3]) / 49,
         ),
     ],
@@ -1461,3 +1458,32 @@ def test_lkjcholeskycov_moment(n, eta, size, expected):
         sd_dist = pm.Exponential.dist(1, size=(*to_tuple(size), n))
         LKJCholeskyCov("x", n=n, eta=eta, sd_dist=sd_dist, size=size, compute_corr=False)
     assert_moment_is_expected(model, expected, check_finite_logp=size is None)
+
+
+@pytest.mark.parametrize(
+    "a, n, size, expected",
+    [
+        (np.array([2, 2, 2, 2]), 1, None, np.array([1, 0, 0, 0])),
+        (np.array([3, 6, 0.5, 0.5]), 2, None, np.array([1, 1, 0, 0])),
+        (np.array([30, 60, 5, 5]), 10, None, np.array([4, 6, 0, 0])),
+        (
+            np.array([[26, 26, 26, 22]]),  # Dim: 1 x 4
+            np.array([[1], [10]]),  # Dim: 2 x 1
+            None,
+            np.array([[[1, 0, 0, 0]], [[2, 3, 3, 2]]]),  # Dim: 2 x 1 x 4
+        ),
+        (
+            np.array([[26, 26, 26, 22]]),  # Dim: 1 x 4
+            np.array([[1], [10]]),  # Dim: 2 x 1
+            (2, 1),
+            np.full(
+                (2, 1, 2, 1, 4),
+                np.array([[[1, 0, 0, 0]], [[2, 3, 3, 2]]]),  # Dim: 2 x 1 x 4
+            ),
+        ),
+    ],
+)
+def test_dirichlet_multinomial_moment(a, n, size, expected):
+    with Model() as model:
+        DirichletMultinomial("x", n=n, a=a, size=size)
+    assert_moment_is_expected(model, expected)