diff --git a/docs/source/utilities.rst b/docs/source/utilities.rst
index 1fc7ad7c7..3cec9aa0b 100644
--- a/docs/source/utilities.rst
+++ b/docs/source/utilities.rst
@@ -40,9 +40,9 @@ log_density
 -----------
 .. autofunction:: numpyro.infer.util.log_density
 
-log_densities
--------------
-.. autofunction:: numpyro.infer.util.log_densities
+compute_log_probs
+-----------------
+.. autofunction:: numpyro.infer.util.compute_log_probs
 
 get_transforms
 --------------
diff --git a/numpyro/infer/util.py b/numpyro/infer/util.py
index 478971228..a3b7425d0 100644
--- a/numpyro/infer/util.py
+++ b/numpyro/infer/util.py
@@ -54,7 +54,7 @@ def process_message(self, msg):
             msg["value"] = random.PRNGKey(0)
 
 
-def log_densities(
+def compute_log_probs(
     model,
     model_args: tuple,
     model_kwargs: dict,
@@ -116,7 +116,7 @@ def log_density(model, model_args: tuple, model_kwargs: dict, params: dict):
     :param params: Dictionary of current parameter values keyed by site name.
     :return: Log of joint density and a corresponding model trace.
     """
-    log_joint, model_trace = log_densities(model, model_args, model_kwargs, params)
+    log_joint, model_trace = compute_log_probs(model, model_args, model_kwargs, params)
     # We need to start with 0.0 instead of 0 because log_joint may be empty or only
     # contain integers, but log_density must be a floating point value to be
     # differentiable by jax.
diff --git a/test/infer/test_infer_util.py b/test/infer/test_infer_util.py
index 0505f16fa..ab0133700 100644
--- a/test/infer/test_infer_util.py
+++ b/test/infer/test_infer_util.py
@@ -27,9 +27,9 @@
 from numpyro.infer.reparam import TransformReparam
 from numpyro.infer.util import (
     Predictive,
+    compute_log_probs,
     constrain_fn,
     initialize_model,
-    log_densities,
     log_density,
     log_likelihood,
     potential_energy,
@@ -268,7 +268,7 @@ def test_log_likelihood(batch_shape):
     )
 
 
-def test_log_densities():
+def test_compute_log_probs():
     model, data, _ = beta_bernoulli()
     samples = Predictive(model, return_sites=["beta"], num_samples=1)(random.key(7))
     samples = {key: value[0] for key, value in samples.items()}
@@ -276,11 +276,11 @@ def test_log_densities():
     logden, _ = log_density(model, (data,), {}, samples)
     assert logden.shape == ()
 
-    logdens, _ = log_densities(model, (data,), {}, samples)
+    logdens, _ = compute_log_probs(model, (data,), {}, samples)
     assert set(logdens) == {"beta", "obs"}
     assert all(x.shape == () for x in logdens.values())
 
-    logdens, _ = log_densities(model, (data,), {}, samples, False)
+    logdens, _ = compute_log_probs(model, (data,), {}, samples, False)
     assert logdens["beta"].shape == (2,)
     assert logdens["obs"].shape == (800, 2)