✨added logit_p to docstring & noted in ReleaseNote

Author: purna135

Diff for: RELEASE-NOTES.md

@@ -133,6 +133,7 @@ This includes API changes we did not warn about since at least `3.11.0` (2021-01
 - Adding support for blackjax's NUTS sampler `pymc.sampling_jax` (see [#5477](ihttps://github.com/pymc-devs/pymc/pull/5477))
 - `pymc.sampling_jax` samplers support `log_likelihood`, `observed_data`, and `sample_stats` in returned InferenceData object (see [#5189](https://github.com/pymc-devs/pymc/pull/5189))
 - Adding support for `pm.Deterministic` in `pymc.sampling_jax` (see [#5182](https://github.com/pymc-devs/pymc/pull/5182))
+- Added an alternative parametrization, `logit_p` to `pm.Binomial` and `pm.Categorical` distributions (see [5637](https://github.com/pymc-devs/pymc/pull/5637)).
 - ...
 
 

Diff for: pymc/distributions/discrete.py

@@ -106,10 +106,12 @@ class Binomial(Discrete):
 
     Parameters
     ----------
-    n: int
+    n : int
         Number of Bernoulli trials (n >= 0).
-    p: float
+    p : float
         Probability of success in each trial (0 < p < 1).
+    logit_p : float
+        Alternative log odds for the probability of success.
     """
     rv_op = binomial
 
@@ -1253,9 +1255,11 @@ class Categorical(Discrete):
 
     Parameters
     ----------
-    p: array of floats
+    p : array of floats
         p > 0 and the elements of p must sum to 1. They will be automatically
         rescaled otherwise.
+    logit_p : float
+        Alternative log odds for the probability of success.
     """
     rv_op = categorical