diff --git a/tests/ensemble_test.py b/tests/ensemble_test.py
index a9fb02235..d7f2a48c1 100644
--- a/tests/ensemble_test.py
+++ b/tests/ensemble_test.py
@@ -4,7 +4,7 @@
 from __future__ import annotations
 
 import pytest
-from torch import eye, ones, zeros
+from torch import eye, ones, randn_like, tensor, zeros
 from torch.distributions import MultivariateNormal
 
 from sbi.inference import NLE_A, NPE_C, NRE_A
@@ -133,9 +133,9 @@ def simulator(theta):
             num_samples=num_samples,
         )
         max_dkl = 0.15
-        assert (
-            dkl < max_dkl
-        ), f"D-KL={dkl} is more than 2 stds above the average performance."
+        assert dkl < max_dkl, (
+            f"D-KL={dkl} is more than 2 stds above the average performance."
+        )
 
     # test individual log_prob and map
     posterior.log_prob(samples, individually=True)
@@ -149,3 +149,42 @@ def simulator(theta):
         samples = posterior.sample_batched((10,), ones(x_o_batch_dim, num_dim))
 
     assert samples.shape == (10, x_o_batch_dim, num_dim), "Sample shape wrong"
+
+
+def test_ensemble_posterior_weights():
+    """Test EnsemblePosterior weight handling for valid and invalid formats."""
+    num_dim = 2
+    ensemble_size = 2
+    num_simulations = 50
+    x_o = zeros(1, num_dim)
+
+    prior = MultivariateNormal(loc=zeros(num_dim), covariance_matrix=eye(num_dim))
+
+    posteriors = []
+    for _ in range(ensemble_size):
+        theta = prior.sample((num_simulations,))
+        x = theta + 0.1 * randn_like(theta)
+        inferer = NPE_C(prior)
+        inferer.append_simulations(theta, x).train(max_num_epochs=1)
+        posteriors.append(inferer.build_posterior())
+
+    posterior = EnsemblePosterior(posteriors)
+    posterior.set_default_x(x_o)
+    _ = posterior.sample((2,))
+
+    posterior = EnsemblePosterior(posteriors, weights=[0.3, 0.7])
+    posterior.set_default_x(x_o)
+    _ = posterior.sample((2,))
+
+    posterior = EnsemblePosterior(posteriors, weights=tensor([0.4, 0.6]))
+    posterior.set_default_x(x_o)
+    _ = posterior.sample((2,))
+
+    with pytest.raises(TypeError):
+        EnsemblePosterior(posteriors, weights={"w1": 0.5, "w2": 0.5})
+
+    with pytest.raises(TypeError):
+        EnsemblePosterior(posteriors, weights=0.5)
+
+    with pytest.raises(TypeError):
+        EnsemblePosterior(posteriors, weights=(0.5, 0.5))