Merge pull request #21759 from rajasekharporeddy:testbranch1

PiperOrigin-RevId: 641831969

Author: jax authors

jax/_src/scipy/stats/_core.py

@@ -142,7 +142,7 @@ def rankdata(
   if nan_policy not in ["propagate", "omit", "raise"]:
     raise ValueError(
       f"Illegal nan_policy value {nan_policy!r}; expected one of "
-      "{'propoagate', 'omit', 'raise'}"
+      "{'propagate', 'omit', 'raise'}"
     )
   if nan_policy == "omit":
     raise NotImplementedError(

jax/_src/scipy/stats/bernoulli.py

@@ -25,7 +25,7 @@ def logpmf(k: ArrayLike, p: ArrayLike, loc: ArrayLike = 0) -> Array:
 
   JAX implementation of :obj:`scipy.stats.bernoulli` ``logpmf``
 
-  The Bernoulli probablility mass function is defined as
+  The Bernoulli probability mass function is defined as
 
   .. math::
 
@@ -62,7 +62,7 @@ def pmf(k: ArrayLike, p: ArrayLike, loc: ArrayLike = 0) -> Array:
 
   JAX implementation of :obj:`scipy.stats.bernoulli` ``pmf``
 
-  The Bernoulli probablility mass function is defined as
+  The Bernoulli probability mass function is defined as
 
   .. math::
 

jax/_src/scipy/stats/multivariate_normal.py

@@ -33,7 +33,7 @@ def logpdf(x: ArrayLike, mean: ArrayLike, cov: ArrayLike, allow_singular: None =
 
      f(x) = \frac{1}{(2\pi)^k\det\Sigma}\exp\left(-\frac{(x-\mu)^T\Sigma^{-1}(x-\mu)}{2} \right)
 
-  where :math:`\mu` is the ``mean``, :math:`\Sigma` is the covarance matrix (``cov``), and
+  where :math:`\mu` is the ``mean``, :math:`\Sigma` is the covariance matrix (``cov``), and
   :math:`k` is the rank of :math:`\Sigma`.
 
   Args:
@@ -83,7 +83,7 @@ def pdf(x: ArrayLike, mean: ArrayLike, cov: ArrayLike) -> Array:
 
      f(x) = \frac{1}{(2\pi)^k\det\Sigma}\exp\left(-\frac{(x-\mu)^T\Sigma^{-1}(x-\mu)}{2} \right)
 
-  where :math:`\mu` is the ``mean``, :math:`\Sigma` is the covarance matrix (``cov``), and
+  where :math:`\mu` is the ``mean``, :math:`\Sigma` is the covariance matrix (``cov``), and
   :math:`k` is the rank of :math:`\Sigma`.
 
   Args:

jax/_src/scipy/stats/norm.py

@@ -175,7 +175,7 @@ def ppf(q: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
     scale: arraylike, distribution scale parameter
 
   Returns:
-    array of ppdf values.
+    array of ppf values.
 
   See Also:
     - :func:`jax.scipy.stats.norm.cdf`