Adds moments for inverse gamma distribution (#5199)

Adds moments for inverse gamma distribution, returns the mode when the mean is undefined

Co-authored-by: Ricardo Vieira <[email protected]>

Author: morganstrom

pymc/distributions/continuous.py

@@ -2334,23 +2334,19 @@ def dist(cls, alpha=None, beta=None, mu=None, sigma=None, sd=None, *args, **kwar
         alpha = at.as_tensor_variable(floatX(alpha))
         beta = at.as_tensor_variable(floatX(beta))
 
-        # m = beta / (alpha - 1.0)
-        # try:
-        #     mean = (alpha > 1) * m or np.inf
-        # except ValueError:  # alpha is an array
-        #     m[alpha <= 1] = np.inf
-        #     mean = m
-
-        # mode = beta / (alpha + 1.0)
-        # variance = at.switch(
-        #     at.gt(alpha, 2), (beta ** 2) / ((alpha - 2) * (alpha - 1.0) ** 2), np.inf
-        # )
-
         assert_negative_support(alpha, "alpha", "InverseGamma")
         assert_negative_support(beta, "beta", "InverseGamma")
 
         return super().dist([alpha, beta], **kwargs)
 
+    def get_moment(rv, size, alpha, beta):
+        mean = beta / (alpha - 1.0)
+        mode = beta / (alpha + 1.0)
+        moment = at.switch(alpha > 1, mean, mode)
+        if not rv_size_is_none(size):
+            moment = at.full(size, moment)
+        return moment
+
     @classmethod
     def _get_alpha_beta(cls, alpha, beta, mu, sigma):
         if alpha is not None:

pymc/tests/test_distributions_moments.py

@@ -31,6 +31,7 @@
     HalfNormal,
     HalfStudentT,
     HyperGeometric,
+    InverseGamma,
     Kumaraswamy,
     Laplace,
     Logistic,
@@ -396,6 +397,21 @@ def test_gamma_moment(alpha, beta, size, expected):
     assert_moment_is_expected(model, expected)
 
 
+@pytest.mark.parametrize(
+    "alpha, beta, size, expected",
+    [
+        (5, 1, None, 1 / 4),
+        (0.5, 1, None, 1 / 1.5),
+        (5, 1, 5, np.full(5, 1 / (5 - 1))),
+        (np.arange(1, 6), 1, None, np.array([0.5, 1, 1 / 2, 1 / 3, 1 / 4])),
+    ],
+)
+def test_inverse_gamma_moment(alpha, beta, size, expected):
+    with Model() as model:
+        InverseGamma("x", alpha=alpha, beta=beta, size=size)
+    assert_moment_is_expected(model, expected)
+
+
 @pytest.mark.parametrize(
     "alpha, m, size, expected",
     [