diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py
index b124866b1f..90684bfa79 100644
--- a/pymc/distributions/continuous.py
+++ b/pymc/distributions/continuous.py
@@ -3350,6 +3350,22 @@ def get_nu_b(cls, nu, b, sigma):
             return nu, b, sigma
         raise ValueError("Rice distribution must specify either nu" " or b.")
 
+    def get_moment(rv, size, nu, sigma):
+        nu_sigma_ratio = -(nu ** 2) / (2 * sigma ** 2)
+        mean = (
+            sigma
+            * np.sqrt(np.pi / 2)
+            * at.exp(nu_sigma_ratio / 2)
+            * (
+                (1 - nu_sigma_ratio) * at.i0(-nu_sigma_ratio / 2)
+                - nu_sigma_ratio * at.i1(-nu_sigma_ratio / 2)
+            )
+        )
+
+        if not rv_size_is_none(size):
+            mean = at.full(size, mean)
+        return mean
+
     def logp(value, b, sigma):
         """
         Calculate log-probability of Rice distribution at specified value.
diff --git a/pymc/tests/test_distributions_moments.py b/pymc/tests/test_distributions_moments.py
index defe31afb6..58d24396ba 100644
--- a/pymc/tests/test_distributions_moments.py
+++ b/pymc/tests/test_distributions_moments.py
@@ -36,6 +36,7 @@
     Normal,
     Pareto,
     Poisson,
+    Rice,
     SkewNormal,
     StudentT,
     Triangular,
@@ -757,7 +758,7 @@ def test_categorical_moment(p, size, expected):
     "mu, sigma, size, expected",
     [
         (4.0, 3.0, None, 7.8110885363844345),
-        (4, np.full(5, 3), None, np.full(5, 7.8110885363844345)),
+        (4.0, np.full(5, 3), None, np.full(5, 7.8110885363844345)),
         (np.arange(5), 1, None, np.arange(5) + 1.2703628454614782),
         (np.arange(5), np.ones(5), (2, 5), np.full((2, 5), np.arange(5) + 1.2703628454614782)),
     ],
@@ -772,7 +773,7 @@ def test_moyal_moment(mu, sigma, size, expected):
     "alpha, mu, sigma, size, expected",
     [
         (1.0, 1.0, 1.0, None, 1.56418958),
-        (1, np.ones(5), 1, None, np.full(5, 1.56418958)),
+        (1.0, np.ones(5), 1.0, None, np.full(5, 1.56418958)),
         (np.ones(5), 1, np.ones(5), None, np.full(5, 1.56418958)),
         (
             np.arange(5),
@@ -822,3 +823,43 @@ def test_asymmetriclaplace_moment(b, kappa, mu, size, expected):
     with Model() as model:
         AsymmetricLaplace("x", b=b, kappa=kappa, mu=mu, size=size)
     assert_moment_is_expected(model, expected)
+
+
+@pytest.mark.parametrize(
+    "nu, sigma, size, expected",
+    [
+        (1.0, 1.0, None, 1.5485724605511453),
+        (1.0, np.ones(5), None, np.full(5, 1.5485724605511453)),
+        (
+            np.arange(1, 6),
+            1.0,
+            None,
+            (
+                1.5485724605511453,
+                2.2723834280687427,
+                3.1725772879007166,
+                4.127193542536757,
+                5.101069639492123,
+            ),
+        ),
+        (
+            np.arange(1, 6),
+            np.ones(5),
+            (2, 5),
+            np.full(
+                (2, 5),
+                (
+                    1.5485724605511453,
+                    2.2723834280687427,
+                    3.1725772879007166,
+                    4.127193542536757,
+                    5.101069639492123,
+                ),
+            ),
+        ),
+    ],
+)
+def test_rice_moment(nu, sigma, size, expected):
+    with Model() as model:
+        Rice("x", nu=nu, sigma=sigma, size=size)
+    assert_moment_is_expected(model, expected)