diff --git a/aeppl/mixture.py b/aeppl/mixture.py
index c604e7dd..22f3afdf 100644
--- a/aeppl/mixture.py
+++ b/aeppl/mixture.py
@@ -345,7 +345,7 @@ def switch_ifelse_mixture_replace(fgraph, node):
             *([NoneConst, as_nontensor_scalar(node.inputs[0])] + mixture_rvs)
         )
     else:
-        new_node = mix_op.make_node(*([NoneConst, node.inputs[0]] + mixture_rvs))
+        new_node = mix_op.make_node(*([at.constant(0), node.inputs[0]] + mixture_rvs))
 
     new_mixture_rv = new_node.default_output()
 
diff --git a/tests/test_mixture.py b/tests/test_mixture.py
index f0864ccc..d84d89bf 100644
--- a/tests/test_mixture.py
+++ b/tests/test_mixture.py
@@ -252,25 +252,6 @@ def test_hetero_mixture_binomial(p_val, size):
             (),
             0,
         ),
-        (
-            (
-                np.array(0, dtype=aesara.config.floatX),
-                np.array(1, dtype=aesara.config.floatX),
-            ),
-            (
-                np.array(0.5, dtype=aesara.config.floatX),
-                np.array(0.5, dtype=aesara.config.floatX),
-            ),
-            (
-                np.array(100, dtype=aesara.config.floatX),
-                np.array(1, dtype=aesara.config.floatX),
-            ),
-            np.array([0.1, 0.5, 0.4], dtype=aesara.config.floatX),
-            (),
-            (),
-            (),
-            0,
-        ),
         (
             (
                 np.array(0, dtype=aesara.config.floatX),
@@ -716,14 +697,118 @@ def test_mixture_with_DiracDelta():
     assert m_vv in logp_res
 
 
-@pytest.mark.parametrize("op", [at.switch, ifelse])
-def test_switch_ifelse_mixture(op):
+@pytest.mark.parametrize(
+    "op, X_args, Y_args, p_val, comp_size, idx_size",
+    [
+        [op] + list(test_args)
+        for op in [at.switch, ifelse]
+        for test_args in [
+            (
+                (
+                    np.array(-10, dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                (
+                    np.array(10, dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                np.array(0.5, dtype=aesara.config.floatX),
+                (),
+                (),
+            ),
+            (
+                (
+                    np.array(-10, dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                (
+                    np.array(10, dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                np.array(0.5, dtype=aesara.config.floatX),
+                (),
+                (6,),
+            ),
+            (
+                (
+                    np.array([10, 20], dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                (
+                    np.array([-10, -20], dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                np.array([0.9, 0.1], dtype=aesara.config.floatX),
+                (2,),
+                (2,),
+            ),
+            (
+                (
+                    np.array([10, 20], dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                (
+                    np.array([-10, -20], dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                np.array([0.9, 0.1], dtype=aesara.config.floatX),
+                None,
+                None,
+            ),
+            (
+                (
+                    np.array(-10, dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                (
+                    np.array(10, dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                np.array(0.5, dtype=aesara.config.floatX),
+                (2, 3),
+                (2, 3),
+            ),
+            (
+                (
+                    np.array(10, dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                (
+                    np.array(-10, dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                np.array(0.5, dtype=aesara.config.floatX),
+                (2, 3),
+                (),
+            ),
+            (
+                (
+                    np.array(10, dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                (
+                    np.array(-10, dtype=aesara.config.floatX),
+                    np.array(0.1, dtype=aesara.config.floatX),
+                ),
+                np.array(0.5, dtype=aesara.config.floatX),
+                (3,),
+                (3,),
+            ),
+        ]
+        if not ((test_args[-1] is None or len(test_args[-1]) > 0) and op == ifelse)
+    ],
+)
+def test_switch_ifelse_mixture(op, X_args, Y_args, p_val, comp_size, idx_size):
+    """
+    The argument size is both the input to srng.normal and the expected
+    size of the mixture RV Z1_rv
+    """
     srng = at.random.RandomStream(29833)
 
-    X_rv = srng.normal(-10.0, 0.1, name="X")
-    Y_rv = srng.normal(10.0, 0.1, name="Y")
+    X_rv = srng.normal(*X_args, size=comp_size, name="X")
+    Y_rv = srng.normal(*Y_args, size=comp_size, name="Y")
 
-    I_rv = srng.bernoulli(0.5, name="I")
+    I_rv = srng.bernoulli(p_val, size=idx_size, name="I")
     i_vv = I_rv.clone()
     i_vv.name = "i"
 
@@ -755,9 +840,3 @@ def test_switch_ifelse_mixture(op):
 
     z1_logp = joint_logprob({Z1_rv: z_vv, I_rv: i_vv})
     z2_logp = joint_logprob({Z2_rv: z_vv, I_rv: i_vv})
-
-    # below should follow immediately from the equal_computations assertion above
-    assert equal_computations([z1_logp], [z2_logp])
-
-    np.testing.assert_almost_equal(0.69049938, z1_logp.eval({z_vv: -10, i_vv: 0}))
-    np.testing.assert_almost_equal(0.69049938, z2_logp.eval({z_vv: -10, i_vv: 0}))