diff --git a/tests/infer/test_valid_models.py b/tests/infer/test_valid_models.py
index 86ab157a05..c67b1b2c74 100644
--- a/tests/infer/test_valid_models.py
+++ b/tests/infer/test_valid_models.py
@@ -2344,6 +2344,58 @@ def guide():
     assert_ok(model, guide, elbo)
 
 
+@pytest.mark.stage("funsor")
+@pytest.mark.parametrize("num_particles", [1, 2])
+def test_collapse_diag_normal_plate_normal(num_particles):
+    T, d = 5, 3
+    data = torch.randn((T, d))
+
+    def model():
+        x = pyro.sample("x", dist.Normal(0., 1.)).unsqueeze(-1)
+        with poutine.collapse():
+            with pyro.plate("data", T, dim=-1):
+                expand_shape = (d,) if num_particles == 1 else (num_particles, 1, d)
+                y = pyro.sample("y", dist.Normal(x, 1.).expand(expand_shape).to_event(1))
+                pyro.sample("z", dist.Normal(y, 1.).to_event(1), obs=data)
+
+    def guide():
+        loc = pyro.param("loc", torch.tensor(0.))
+        scale = pyro.param("scale", torch.tensor(1.), constraint=constraints.positive)
+        pyro.sample("x", dist.Normal(loc, scale))
+
+    elbo = Trace_ELBO(num_particles=num_particles, vectorize_particles=True,
+                      max_plate_nesting=1)
+    assert_ok(model, guide, elbo)
+
+
+@pytest.mark.stage("funsor")
+@pytest.mark.parametrize("num_particles", [1, 2])
+def test_collapse_normal_mvn_mvn(num_particles):
+    T, d, S = 5, 3, 4
+    data = torch.randn((T, d, S))
+
+    def model():
+        x = pyro.sample("x", dist.Normal(0., 1.)).unsqueeze(-1)
+        with poutine.collapse():
+            with pyro.plate("d", d, dim=-1):
+                expand_shape = (d, S) if num_particles == 1 else (num_particles, d, S)
+                beta0 = pyro.sample("beta0", dist.Normal(x, 1.).expand(expand_shape).to_event(1))
+                beta = pyro.sample("beta", dist.MultivariateNormal(beta0, torch.eye(S)))
+
+            mean = torch.ones((T, d)) @ beta
+            with pyro.plate("data", T, dim=-1):
+                pyro.sample("obs", dist.MultivariateNormal(mean, torch.eye(S)), obs=data)
+
+    def guide():
+        loc = pyro.param("loc", torch.tensor(0.))
+        scale = pyro.param("scale", torch.tensor(1.), constraint=constraints.positive)
+        pyro.sample("x", dist.Normal(loc, scale))
+
+    elbo = Trace_ELBO(num_particles=num_particles, vectorize_particles=True,
+                      max_plate_nesting=1)
+    assert_ok(model, guide, elbo)
+
+
 @pytest.mark.stage("funsor")
 @pytest.mark.parametrize("num_particles", [1, 2])
 def test_collapse_beta_bernoulli(num_particles):