diff --git a/pymc/distributions/multivariate.py b/pymc/distributions/multivariate.py
index 99568861bf..265d535b48 100644
--- a/pymc/distributions/multivariate.py
+++ b/pymc/distributions/multivariate.py
@@ -2192,32 +2192,23 @@ def make_node(self, rng, size, dtype, alpha, K):
         alpha = at.as_tensor_variable(alpha)
         K = at.as_tensor_variable(intX(K))
 
-        if alpha.ndim > 0:
-            raise ValueError("The concentration parameter needs to be a scalar.")
-
         if K.ndim > 0:
             raise ValueError("K must be a scalar.")
 
         return super().make_node(rng, size, dtype, alpha, K)
 
-    def _infer_shape(self, size, dist_params, param_shapes=None):
-        alpha, K = dist_params
-
-        size = tuple(size)
-
-        return size + (K + 1,)
+    def _supp_shape_from_params(self, dist_params, **kwargs):
+        K = dist_params[1]
+        return (K + 1,)
 
     @classmethod
     def rng_fn(cls, rng, alpha, K, size):
         if K < 0:
             raise ValueError("K needs to be positive.")
 
-        if size is None:
-            size = (K,)
-        elif isinstance(size, int):
-            size = (size,) + (K,)
-        else:
-            size = tuple(size) + (K,)
+        size = to_tuple(size) if size is not None else alpha.shape
+        size = size + (K,)
+        alpha = alpha[..., np.newaxis]
 
         betas = rng.beta(1, alpha, size=size)
 
@@ -2286,9 +2277,10 @@ def dist(cls, alpha, K, *args, **kwargs):
         return super().dist([alpha, K], **kwargs)
 
     def moment(rv, size, alpha, K):
+        alpha = alpha[..., np.newaxis]
         moment = (alpha / (1 + alpha)) ** at.arange(K)
         moment *= 1 / (1 + alpha)
-        moment = at.concatenate([moment, [(alpha / (1 + alpha)) ** K]], axis=-1)
+        moment = at.concatenate([moment, (alpha / (1 + alpha)) ** K], axis=-1)
         if not rv_size_is_none(size):
             moment_size = at.concatenate(
                 [
diff --git a/pymc/tests/test_distributions.py b/pymc/tests/test_distributions.py
index 28705ba1b8..d0c1135bdd 100644
--- a/pymc/tests/test_distributions.py
+++ b/pymc/tests/test_distributions.py
@@ -23,6 +23,7 @@
 from aeppl.logprob import ParameterValueError
 from aesara.tensor.random.utils import broadcast_params
 
+from pymc.aesaraf import compile_pymc
 from pymc.distributions.continuous import get_tau_sigma
 from pymc.util import UNSET
 
@@ -952,6 +953,17 @@ def test_hierarchical_obs_logp():
     assert not any(isinstance(o, RandomVariable) for o in ops)
 
 
+@pytest.fixture(scope="module")
+def stickbreakingweights_logpdf():
+    _value = at.vector()
+    _alpha = at.scalar()
+    _k = at.iscalar()
+    _logp = logp(StickBreakingWeights.dist(_alpha, _k), _value)
+    core_fn = compile_pymc([_value, _alpha, _k], _logp)
+
+    return np.vectorize(core_fn, signature="(n),(),()->()")
+
+
 class TestMatchesScipy:
     def test_uniform(self):
         check_logp(
@@ -2312,6 +2324,25 @@ def test_stickbreakingweights_invalid(self):
         assert pm.logp(sbw, np.array([0.4, 0.3, 0.2, -0.1])).eval() == -np.inf
         assert pm.logp(sbw_wrong_K, np.array([0.4, 0.3, 0.2, 0.1])).eval() == -np.inf
 
+    @pytest.mark.parametrize(
+        "alpha,K",
+        [
+            (np.array([0.5, 1.0, 2.0]), 3),
+            (np.arange(1, 7, dtype="float64").reshape(2, 3), 5),
+        ],
+    )
+    def test_stickbreakingweights_vectorized(self, alpha, K, stickbreakingweights_logpdf):
+        value = pm.StickBreakingWeights.dist(alpha, K).eval()
+        with Model():
+            sbw = StickBreakingWeights("sbw", alpha=alpha, K=K, transform=None)
+        pt = {"sbw": value}
+        assert_almost_equal(
+            pm.logp(sbw, value).eval(),
+            stickbreakingweights_logpdf(value, alpha, K),
+            decimal=select_by_precision(float64=6, float32=2),
+            err_msg=str(pt),
+        )
+
     @aesara.config.change_flags(compute_test_value="raise")
     def test_categorical_bounds(self):
         with Model():
diff --git a/pymc/tests/test_distributions_moments.py b/pymc/tests/test_distributions_moments.py
index 75c29849bc..fac192315c 100644
--- a/pymc/tests/test_distributions_moments.py
+++ b/pymc/tests/test_distributions_moments.py
@@ -1166,6 +1166,32 @@ def test_rice_moment(nu, sigma, size, expected):
                 fill_value=np.append((1 / 3) ** np.arange(5) * 2 / 3, (1 / 3) ** 5),
             ),
         ),
+        (
+            np.array([1, 3]),
+            11,
+            None,
+            np.array(
+                [
+                    np.append((1 / 2) ** np.arange(11) * 1 / 2, (1 / 2) ** 11),
+                    np.append((3 / 4) ** np.arange(11) * 1 / 4, (3 / 4) ** 11),
+                ]
+            ),
+        ),
+        (
+            np.array([1, 3, 5]),
+            9,
+            (5, 3),
+            np.full(
+                shape=(5, 3, 10),
+                fill_value=np.array(
+                    [
+                        np.append((1 / 2) ** np.arange(9) * 1 / 2, (1 / 2) ** 9),
+                        np.append((3 / 4) ** np.arange(9) * 1 / 4, (3 / 4) ** 9),
+                        np.append((5 / 6) ** np.arange(9) * 1 / 6, (5 / 6) ** 9),
+                    ]
+                ),
+            ),
+        ),
     ],
 )
 def test_stickbreakingweights_moment(alpha, K, size, expected):
diff --git a/pymc/tests/test_distributions_random.py b/pymc/tests/test_distributions_random.py
index 5818c5adf5..de7e6cd088 100644
--- a/pymc/tests/test_distributions_random.py
+++ b/pymc/tests/test_distributions_random.py
@@ -1321,6 +1321,18 @@ def check_basic_properties(self):
         assert np.all(draws <= 1)
 
 
+class TestStickBreakingWeights_1D_alpha(BaseTestDistributionRandom):
+    pymc_dist = pm.StickBreakingWeights
+    pymc_dist_params = {"alpha": [1.0, 2.0, 3.0], "K": 19}
+    expected_rv_op_params = {"alpha": [1.0, 2.0, 3.0], "K": 19}
+    sizes_to_check = [None, (3,), (5, 3)]
+    sizes_expected = [(3, 20), (3, 20), (5, 3, 20)]
+    checks_to_run = [
+        "check_pymc_params_match_rv_op",
+        "check_rv_size",
+    ]
+
+
 class TestCategorical(BaseTestDistributionRandom):
     pymc_dist = pm.Categorical
     pymc_dist_params = {"p": np.array([0.28, 0.62, 0.10])}