Binomial and Poisson Moment

pymc/distributions/discrete.py

@@ -114,9 +114,14 @@ class Binomial(Discrete):
     def dist(cls, n, p, *args, **kwargs):
         n = at.as_tensor_variable(intX(n))
         p = at.as_tensor_variable(floatX(p))
-        # mode = at.cast(tround(n * p), self.dtype)
         return super().dist([n, p], **kwargs)
 
+    def get_moment(rv, size, n, p):
+        mean = at.round(n * p)
+        if not rv_size_is_none(size):
+            mean = at.full(size, mean)
+        return mean
+
     def logp(value, n, p):
         r"""
         Calculate log-probability of Binomial distribution at specified value.
@@ -567,9 +572,14 @@ class Poisson(Discrete):
     @classmethod
     def dist(cls, mu, *args, **kwargs):
         mu = at.as_tensor_variable(floatX(mu))
-        # mode = intX(at.floor(mu))
         return super().dist([mu], *args, **kwargs)
 
+    def get_moment(rv, size, mu):
+        mu = at.floor(mu)
+        if not rv_size_is_none(size):
+            mu = at.full(size, mu)
+        return mu
+
     def logp(value, mu):
         r"""
         Calculate log-probability of Poisson distribution at specified value.

pymc/tests/test_distributions_moments.py

@@ -6,6 +6,7 @@
 from pymc import Bernoulli, Flat, HalfFlat, Normal, TruncatedNormal, Uniform
 from pymc.distributions import (
     Beta,
+    Binomial,
     Cauchy,
     Exponential,
     Gamma,
@@ -14,6 +15,7 @@
     Kumaraswamy,
     Laplace,
     LogNormal,
+    Poisson,
     StudentT,
     Weibull,
 )
@@ -209,7 +211,13 @@ def test_laplace_moment(mu, b, size, expected):
         (0, 1, 1, None, 0),
         (0, np.ones(5), 1, None, np.zeros(5)),
         (np.arange(5), 10, np.arange(1, 6), None, np.arange(5)),
-        (np.arange(5), 10, np.arange(1, 6), (2, 5), np.full((2, 5), np.arange(5))),
+        (
+            np.arange(5),
+            10,
+            np.arange(1, 6),
+            (2, 5),
+            np.full((2, 5), np.arange(5)),
+        ),
     ],
 )
 def test_studentt_moment(mu, nu, sigma, size, expected):
@@ -318,11 +326,44 @@ def test_gamma_moment(alpha, beta, size, expected):
             np.arange(1, 6),
             np.arange(2, 7),
             (2, 5),
-            np.full((2, 5), np.arange(2, 7) * special.gamma(1 + 1 / np.arange(1, 6))),
+            np.full(
+                (2, 5),
+                np.arange(2, 7) * special.gamma(1 + 1 / np.arange(1, 6)),
+            ),
         ),
     ],
 )
 def test_weibull_moment(alpha, beta, size, expected):
     with Model() as model:
         Weibull("x", alpha=alpha, beta=beta, size=size)
     assert_moment_is_expected(model, expected)
+
+
+@pytest.mark.parametrize(
+    "n, p, size, expected",
+    [
+        (7, 0.7, None, 5),
+        (7, 0.3, 5, np.full(5, 2)),
+        (10, np.arange(1, 6) / 10, None, np.arange(1, 6)),
+        (10, np.arange(1, 6) / 10, (2, 5), np.full((2, 5), np.arange(1, 6))),
+    ],
+)
+def test_binomial_moment(n, p, size, expected):
+    with Model() as model:
+        Binomial("x", n=n, p=p, size=size)
+    assert_moment_is_expected(model, expected)
+
+
+@pytest.mark.parametrize(
+    "mu, size, expected",
+    [
+        (2.7, None, 2),
+        (2.3, 5, np.full(5, 2)),
+        (np.arange(1, 5), None, np.arange(1, 5)),
+        (np.arange(1, 5), (2, 4), np.full((2, 4), np.arange(1, 5))),
+    ],
+)
+def test_poisson_moment(mu, size, expected):
+    with Model() as model:
+        Poisson("x", mu=mu, size=size)
+    assert_moment_is_expected(model, expected)