Add icdf functions for Beta, Gamma, Chisquared and Students distributions

removing unnecessary parameterization test on studentT test
removing unnecessary test on chisquared test

Author: amyoshino

pymc/distributions/continuous.py

@@ -33,7 +33,7 @@
 from pytensor.raise_op import Assert
 from pytensor.tensor import gammaln
 from pytensor.tensor.extra_ops import broadcast_shape
-from pytensor.tensor.math import tanh
+from pytensor.tensor.math import betaincinv, gammaincinv, tanh
 from pytensor.tensor.random.basic import (
     BetaRV,
     _gamma,
@@ -1227,6 +1227,16 @@ def logcdf(value, alpha, beta):
             msg="alpha > 0, beta > 0",
         )
 
+    def icdf(value, alpha, beta):
+        res = betaincinv(alpha, beta, value)
+        res = check_icdf_value(res, value)
+        return check_icdf_parameters(
+            res,
+            alpha > 0,
+            beta > 0,
+            msg="alpha > 0, beta > 0",
+        )
+
 
 class KumaraswamyRV(RandomVariable):
     name = "kumaraswamy"
@@ -1872,6 +1882,21 @@ def logcdf(value, nu, mu, sigma):
             msg="nu > 0, sigma > 0",
         )
 
+    def icdf(value, nu, mu, sigma):
+        res = pt.switch(
+            pt.lt(value, 0.5),
+            -pt.sqrt(nu) * pt.sqrt((1.0 / betaincinv(nu * 0.5, 0.5, 2.0 * value)) - 1.0),
+            pt.sqrt(nu) * pt.sqrt((1.0 / betaincinv(nu * 0.5, 0.5, 2.0 * (1 - value))) - 1.0),
+        )
+        res = mu + res * sigma
+        res = check_icdf_value(res, value)
+        return check_icdf_parameters(
+            res,
+            nu > 0,
+            sigma > 0,
+            msg="nu > 0, sigma > 0",
+        )
+
 
 class Pareto(BoundedContinuous):
     r"""
@@ -2272,6 +2297,16 @@ def logcdf(value, alpha, scale):
         )
         return check_parameters(res, 0 < alpha, 0 < beta, msg="alpha > 0, beta > 0")
 
+    def icdf(value, alpha, scale):
+        res = scale * gammaincinv(alpha, value)
+        res = check_icdf_value(res, value)
+        return check_icdf_parameters(
+            res,
+            alpha > 0,
+            scale > 0,
+            msg="alpha > 0, beta > 0",
+        )
+
 
 class InverseGamma(PositiveContinuous):
     r"""

tests/distributions/test_continuous.py

@@ -411,6 +411,13 @@ def test_beta_logcdf(self):
             lambda value, alpha, beta: st.beta.logcdf(value, alpha, beta),
         )
 
+    def test_beta_icdf(self):
+        check_icdf(
+            pm.Beta,
+            {"alpha": Rplus, "beta": Rplus},
+            lambda q, alpha, beta: st.beta.ppf(q, alpha, beta),
+        )
+
     def test_kumaraswamy(self):
         # Scipy does not have a built-in Kumaraswamy
         def scipy_log_pdf(value, a, b):
@@ -557,6 +564,13 @@ def test_studentt_logcdf(self):
             lambda value, nu, mu, sigma: st.t.logcdf(value, nu, mu, sigma),
         )
 
+    def test_studentt_icdf(self):
+        check_icdf(
+            pm.StudentT,
+            {"nu": Rplusbig, "mu": R, "sigma": Rplusbig},
+            lambda q, nu, mu, sigma: st.t.ppf(q, nu, mu, sigma),
+        )
+
     def test_cauchy(self):
         check_logp(
             pm.Cauchy,
@@ -623,6 +637,13 @@ def test_gamma_logcdf(self):
             lambda value, alpha, beta: st.gamma.logcdf(value, alpha, scale=1.0 / beta),
         )
 
+    def test_gamma_icdf(self):
+        check_icdf(
+            pm.Gamma,
+            {"alpha": Rplusbig, "beta": Rplusbig},
+            lambda q, alpha, beta: st.gamma.ppf(q, alpha, scale=1.0 / beta),
+        )
+
     def test_inverse_gamma_logp(self):
         check_logp(
             pm.InverseGamma,