Add Weibull moment

Author: lucianopaz

pymc/distributions/continuous.py

@@ -2514,6 +2514,12 @@ def dist(cls, alpha, beta, *args, **kwargs):
 
         return super().dist([alpha, beta], *args, **kwargs)
 
+    def get_moment(rv, size, alpha, beta):
+        mean = beta * at.gamma(1 + 1 / alpha)
+        if not rv_size_is_none(size):
+            mean = at.full(size, mean)
+        return mean
+
     def logcdf(value, alpha, beta):
         r"""
         Compute the log of the cumulative distribution function for Weibull distribution

pymc/tests/test_distributions_moments.py

@@ -1,6 +1,8 @@
 import numpy as np
 import pytest
 
+from scipy import special
+
 from pymc import Bernoulli, Flat, HalfFlat, Normal, TruncatedNormal, Uniform
 from pymc.distributions import (
     Beta,
@@ -13,6 +15,7 @@
     Laplace,
     LogNormal,
     StudentT,
+    Weibull,
 )
 from pymc.distributions.shape_utils import rv_size_is_none
 from pymc.initial_point import make_initial_point_fn
@@ -303,3 +306,23 @@ def test_gamma_moment(alpha, beta, size, expected):
     with Model() as model:
         Gamma("x", alpha=alpha, beta=beta, size=size)
     assert_moment_is_expected(model, expected)
+
+
+@pytest.mark.parametrize(
+    "alpha, beta, size, expected",
+    [
+        (1, 1, None, 1),
+        (1, 1, 5, np.full(5, 1)),
+        (np.arange(1, 6), 1, None, special.gamma(1 + 1 / np.arange(1, 6))),
+        (
+            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))),
+        ),
+    ],
+)
+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)