From e395b2ea485195b1deec8e2dd05b33fd05514ef6 Mon Sep 17 00:00:00 2001
From: Somasree Majumder <56045049+soma2000-lang@users.noreply.github.com>
Date: Sat, 4 Feb 2023 01:56:42 +0530
Subject: [PATCH 1/2] Update continuous.py

---
 pymc/distributions/continuous.py | 19 -------------------
 1 file changed, 19 deletions(-)

diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py
index d587a86153..283ceebb5e 100644
--- a/pymc/distributions/continuous.py
+++ b/pymc/distributions/continuous.py
@@ -2332,25 +2332,6 @@ def logcdf(value, nu):
         return logcdf(Gamma.dist(alpha=nu / 2, beta=0.5), value)
 
 
-# TODO: Remove this once logp for multiplication is working!
-class WeibullBetaRV(RandomVariable):
-    name = "weibull"
-    ndim_supp = 0
-    ndims_params = [0, 0]
-    dtype = "floatX"
-    _print_name = ("Weibull", "\\operatorname{Weibull}")
-
-    def __call__(self, alpha, beta, size=None, **kwargs):
-        return super().__call__(alpha, beta, size=size, **kwargs)
-
-    @classmethod
-    def rng_fn(cls, rng, alpha, beta, size) -> np.ndarray:
-        return np.asarray(beta * rng.weibull(alpha, size=size))
-
-
-weibull_beta = WeibullBetaRV()
-
-
 class Weibull(PositiveContinuous):
     r"""
     Weibull log-likelihood.

From e257de3179560b5b27fbcd4ae7b0beba7f59ecd0 Mon Sep 17 00:00:00 2001
From: Somasree Majumder <56045049+soma2000-lang@users.noreply.github.com>
Date: Sat, 4 Feb 2023 02:14:35 +0530
Subject: [PATCH 2/2] Update continuous.py

---
 pymc/distributions/continuous.py | 41 +-------------------------------
 1 file changed, 1 insertion(+), 40 deletions(-)

diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py
index 283ceebb5e..9e5cca4832 100644
--- a/pymc/distributions/continuous.py
+++ b/pymc/distributions/continuous.py
@@ -835,22 +835,7 @@ def logcdf(value, loc, sigma):
             sigma > 0,
             msg="sigma > 0",
         )
-
-
-class WaldRV(RandomVariable):
-    name = "wald"
-    ndim_supp = 0
-    ndims_params = [0, 0, 0]
-    dtype = "floatX"
-    _print_name = ("Wald", "\\operatorname{Wald}")
-
-    @classmethod
-    def rng_fn(cls, rng, mu, lam, alpha, size) -> np.ndarray:
-        return np.asarray(rng.wald(mu, lam, size=size) + alpha)
-
-
-wald = WaldRV()
-
+        
 
 class Wald(PositiveContinuous):
     r"""
@@ -1015,15 +1000,6 @@ def logcdf(value, mu, lam, alpha):
         )
 
 
-class BetaClippedRV(BetaRV):
-    @classmethod
-    def rng_fn(cls, rng, alpha, beta, size) -> np.ndarray:
-        return np.asarray(clipped_beta_rvs(alpha, beta, size=size, random_state=rng))
-
-
-beta = BetaClippedRV()
-
-
 class Beta(UnitContinuous):
     r"""
     Beta log-likelihood.
@@ -1660,21 +1636,6 @@ def logcdf(value, mu, sigma):
 Lognormal = LogNormal
 
 
-class StudentTRV(RandomVariable):
-    name = "studentt"
-    ndim_supp = 0
-    ndims_params = [0, 0, 0]
-    dtype = "floatX"
-    _print_name = ("StudentT", "\\operatorname{StudentT}")
-
-    @classmethod
-    def rng_fn(cls, rng, nu, mu, sigma, size=None) -> np.ndarray:
-        return np.asarray(stats.t.rvs(nu, mu, sigma, size=size, random_state=rng))
-
-
-studentt = StudentTRV()
-
-
 class StudentT(Continuous):
     r"""
     Student's T log-likelihood.