diff --git a/spec/draft/API_specification/statistical_functions.rst b/spec/draft/API_specification/statistical_functions.rst
index eb5e1a5d6..b54bec5df 100644
--- a/spec/draft/API_specification/statistical_functions.rst
+++ b/spec/draft/API_specification/statistical_functions.rst
@@ -18,6 +18,7 @@ Objects in API
:toctree: generated
:template: method.rst
+ bincount
cumulative_prod
cumulative_sum
max
diff --git a/src/array_api_stubs/_draft/statistical_functions.py b/src/array_api_stubs/_draft/statistical_functions.py
index 55c84950c..e32302904 100644
--- a/src/array_api_stubs/_draft/statistical_functions.py
+++ b/src/array_api_stubs/_draft/statistical_functions.py
@@ -1,4 +1,5 @@
__all__ = [
+ "bincount",
"cumulative_sum",
"cumulative_prod",
"max",
@@ -14,6 +15,45 @@
from ._types import Optional, Tuple, Union, array, dtype
+def bincount(
+ x: array, /, weights: Optional[array] = None, *, minlength: int = 0
+) -> array:
+ """
+ Counts the number of occurrences of each element in ``x``.
+
+ .. admonition:: Data-dependent output shape
+ :class: important
+
+ The shape of the output array for this function depends on the data values in ``x``; hence, array libraries which build computation graphs (e.g., JAX, Dask, etc.) can find this function difficult to implement without knowing the values in ``x``. Accordingly, such libraries **may** choose to omit this function. See :ref:`data-dependent-output-shapes` section for more details.
+
+ Parameters
+ ----------
+ x: array
+ input array. **Should** be a one-dimensional array. **Must** have an integer data type.
+ weights: Optional[array]
+ an array of weights for each element in ``x``. **Must** have the same shape as ``x``. **Must** have a numeric data type. If not provided, each bin in the returned array **must** give the number of occurrences of its index value in ``x``. If provided, each bin in the returned array **must** be a sum of the weights corresponding to the respective index values in ``x`` (i.e., if value ``n`` is found at index ``i`` in ``x``, then ``out[n] += weights[i]``, instead of ``out[n] += 1``). Default: ``None``.
+ minlength: int
+ minimum number of bins. **Must** be a nonnegative integer. Default: ``0``.
+
+ Returns
+ -------
+ out: array
+ an array containing the number of occurrences. Let ``N`` equal ``max(xp.max(x)+1, minlength)``. The returned array **should** have shape ``(N,)``.
+
+ If ``weights`` is not ``None``, the returned array **must** have the same data type as ``weights``.
+
+ If ``weights`` is ``None``, the returned array **must** have the same data type as ``x``, unless ``x`` has an integer data type supporting a smaller range of values than the default integer data type (e.g., ``x`` has an ``int16`` or ``uint32`` data type and the default integer data type is ``int64``). In those latter cases:
+
+ - if ``x`` has a signed integer data type (e.g., ``int16``), the returned array **must** have the default integer data type.
+ - if ``x`` has an unsigned integer data type (e.g., ``uint16``), the returned array **must** have an unsigned integer data type having the same number of bits as the default integer data type (e.g., if the default integer data type is ``int32``, the returned array **must** have a ``uint32`` data type).
+
+ Notes
+ -----
+
+ - If ``x`` contains negative values, behavior is unspecified and thus implementation-defined.
+ """
+
+
def cumulative_prod(
x: array,
/,
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