diff --git a/tests/ensemble_test.py b/tests/ensemble_test.py
index a9fb02235..837d02e74 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
@@ -149,3 +149,40 @@ 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"
+
+
+@pytest.mark.parametrize(
+    "weights, expected_exception",
+    [
+        (None, None),
+        ([0.3, 0.7], None),
+        (tensor([0.4, 0.6]), None),
+        ({"w1": 0.5, "w2": 0.5}, TypeError),
+        (0.5, TypeError),
+        ((0.5, 0.5), TypeError),
+    ],
+)
+def test_ensemble_posterior_weights(weights, expected_exception):
+    """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())
+
+    if expected_exception:
+        with pytest.raises(expected_exception):
+            EnsemblePosterior(posteriors, weights=weights)
+    else:
+        posterior = EnsemblePosterior(posteriors, weights=weights)
+        posterior.set_default_x(x_o)
+        _ = posterior.sample((2,))