diff --git a/tensorflow_probability/python/experimental/mcmc/BUILD b/tensorflow_probability/python/experimental/mcmc/BUILD
index 633e529330..49d42aafcd 100644
--- a/tensorflow_probability/python/experimental/mcmc/BUILD
+++ b/tensorflow_probability/python/experimental/mcmc/BUILD
@@ -704,6 +704,7 @@ multi_substrate_py_library(
         "//tensorflow_probability/python/internal:prefer_static",
         "//tensorflow_probability/python/internal:tensor_util",
         "//tensorflow_probability/python/internal:tensorshape_util",
+        "//tensorflow_probability/python/math:generic",
         "//tensorflow_probability/python/math:gradient",
         "//tensorflow_probability/python/mcmc/internal:util",
     ],
diff --git a/tensorflow_probability/python/experimental/mcmc/weighted_resampling.py b/tensorflow_probability/python/experimental/mcmc/weighted_resampling.py
index 9632d916f5..2dd9e1570f 100644
--- a/tensorflow_probability/python/experimental/mcmc/weighted_resampling.py
+++ b/tensorflow_probability/python/experimental/mcmc/weighted_resampling.py
@@ -21,6 +21,7 @@
 from tensorflow_probability.python.distributions import uniform
 from tensorflow_probability.python.internal import distribution_util as dist_util
 from tensorflow_probability.python.internal import prefer_static as ps
+from tensorflow_probability.python.math.generic import log_cumsum_exp
 from tensorflow_probability.python.math.gradient import value_and_gradient
 from tensorflow_probability.python.mcmc.internal import util as mcmc_util
 
@@ -134,7 +135,7 @@ def _resample_using_log_points(log_probs, sample_shape, log_points, name=None):
          tf.zeros(points_shape, dtype=tf.int32)],
         axis=-1)
     log_marker_positions = tf.broadcast_to(
-        tf.math.cumulative_logsumexp(log_probs, axis=-1),
+        log_cumsum_exp(log_probs, axis=-1),
         markers_shape)
     log_markers_and_points = ps.concat(
         [log_marker_positions, log_points], axis=-1)
diff --git a/tensorflow_probability/python/internal/backend/numpy/BUILD b/tensorflow_probability/python/internal/backend/numpy/BUILD
index 2695b67c85..cc7c09fd19 100644
--- a/tensorflow_probability/python/internal/backend/numpy/BUILD
+++ b/tensorflow_probability/python/internal/backend/numpy/BUILD
@@ -469,7 +469,7 @@ py_test(
         "--test_mode=xla",
         # TODO(b/168718272): reduce_*([nan, nan], axis=0) (GPU)
         # histogram_fixed_width_bins fails with f32([0.]), [0.0, 0.0], 2
-        "--xla_disabled=math.cumulative_logsumexp,math.reduce_min,math.reduce_max,histogram_fixed_width_bins",
+        "--xla_disabled=math.reduce_min,math.reduce_max,histogram_fixed_width_bins",
     ],
     main = "numpy_test.py",
     shard_count = 11,
diff --git a/tensorflow_probability/python/internal/backend/numpy/numpy_math.py b/tensorflow_probability/python/internal/backend/numpy/numpy_math.py
index 40b3a2526b..69c4545519 100644
--- a/tensorflow_probability/python/internal/backend/numpy/numpy_math.py
+++ b/tensorflow_probability/python/internal/backend/numpy/numpy_math.py
@@ -61,7 +61,6 @@
     'count_nonzero',
     'cumprod',
     'cumsum',
-    'cumulative_logsumexp',
     'digamma',
     'divide',
     'divide_no_nan',
@@ -261,23 +260,6 @@ def _cumop(op, x, axis=0, exclusive=False, reverse=False, name=None,
 _cumsum = utils.partial(_cumop, np.cumsum, initial_value=0.)
 
 
-def _cumulative_logsumexp(x, axis=0, exclusive=False, reverse=False, name=None):
-  del name
-  axis = int(axis)
-  if axis < 0:
-    axis = axis + len(x.shape)
-  if JAX_MODE:
-    op = jax.lax.cumlogsumexp
-  else:
-    op = np.logaddexp.accumulate
-  return _cumop(
-      op, x,
-      axis=axis,
-      exclusive=exclusive,
-      reverse=reverse,
-      initial_value=-np.inf)
-
-
 def _equal(x, y, name=None):
   del name
   x = _convert_to_tensor(x)
@@ -578,11 +560,6 @@ def _unsorted_segment_sum(data, segment_ids, num_segments, name=None):
     'tf.math.cumsum',
     _cumsum)
 
-cumulative_logsumexp = utils.copy_docstring(
-    'tf.math.cumulative_logsumexp',
-    _cumulative_logsumexp)
-
-
 digamma = utils.copy_docstring(
     'tf.math.digamma',
     lambda x, name=None: scipy_special.digamma(x))
diff --git a/tensorflow_probability/python/internal/backend/numpy/numpy_test.py b/tensorflow_probability/python/internal/backend/numpy/numpy_test.py
index e4405744ff..313aa4b9f4 100644
--- a/tensorflow_probability/python/internal/backend/numpy/numpy_test.py
+++ b/tensorflow_probability/python/internal/backend/numpy/numpy_test.py
@@ -1203,16 +1203,6 @@ def _not_implemented(*args, **kwargs):
                 hps.booleans()).map(lambda x: x[0] + (x[1], x[2]))
         ],
         xla_const_args=(1, 2, 3)),
-    TestCase(
-        'math.cumulative_logsumexp', [
-            hps.tuples(
-                array_axis_tuples(
-                    elements=floats(min_value=-1e12, max_value=1e12)),
-                hps.booleans(),
-                hps.booleans()).map(lambda x: x[0] + (x[1], x[2]))
-        ],
-        rtol=6e-5,
-        xla_const_args=(1, 2, 3)),
 ]
 
 NUMPY_TEST_CASES += [  # break the array for pylint to not timeout.
diff --git a/tensorflow_probability/python/math/generic.py b/tensorflow_probability/python/math/generic.py
index eb72fcda99..0e34391ce2 100644
--- a/tensorflow_probability/python/math/generic.py
+++ b/tensorflow_probability/python/math/generic.py
@@ -30,7 +30,6 @@
 from tensorflow_probability.python.internal import tensorshape_util
 from tensorflow_probability.python.internal import variadic_reduce
 from tensorflow_probability.python.math.scan_associative import scan_associative
-from tensorflow.python.util import deprecation  # pylint: disable=g-direct-tensorflow-import
 
 
 __all__ = [
@@ -90,9 +89,6 @@ def log_combinations(n, counts, name='log_combinations'):
 
 # TODO(b/154562929): Remove this once the built-in op supports XLA.
 # TODO(b/156297366): Derivatives of this function may not always be correct.
-@deprecation.deprecated('2023-03-01',
-                        '`log_cumsum_exp` is deprecated; '
-                        ' Use `tf.math.cumulative_logsumexp` instead.')
 def log_cumsum_exp(x, axis=-1, name=None):
   """Computes log(cumsum(exp(x))).