Define continuous, non-analytical pdfs in python

Question

I am planning to do some basic algebra on continuous, non-analytical random variabels. I want to define their probability density functions as arrays x and f(x).

Yet, I was surprised to find out that there does not seem to be any package that does basic operations like computing sum- or product distributions of two pdfs (please correct me if I'm wrong). To implement those operations by myself, I then planned to create a subclass of scipy.stats rv_continuous, following this thread:

import scipy as sp
import numpy as np
class my_pdf(sp.stats.rv_continuous):
    def init(self,x,p):
        self.x = x
        self.p = p
def _pdf(self,x):
    return sp.interpolate.interp1d(self.x,self.p)(x)


x = np.linspace(0,1,101)
f = 3x*2
my_cv = my_pdf(x,f)
my_cv.pdf(0)

However, overwriting the init method is probably not the way to go. Is there a way to add additional parameters to rv_continuous, or another way to approach the problem, other than building everything from scratch?

the linked question was well answered in this forum, and I feel like scientist should be much more interested in pdfs than developers. However I'll try it, and post the answer here if I get one. thx — Yann, Oct 30 '20 at 21:16
Your question has nothing to do with statistics and everything to do with how Python loads and overwrites modules — , Oct 30 '20 at 21:27
I don't even know if I am on the right track here using rv_continuous, so any other approach is warmly welcome, too — Yann, Oct 30 '20 at 23:58
This question was answered on SO: https://stackoverflow.com/q/64615973 — , Nov 04 '20 at 15:08
Indeed, I did not post the answer here. So you were right about SO, thanks for your hint :) — Yann, Nov 05 '20 at 14:17

score 0 · Accepted Answer · answered Nov 05 '20 at 14:16

So, if anyone stumbles over this again, it got answered here:

import scipy as sp
import scipy.stats
import numpy as np
class my_pdf(sp.stats.rv_continuous):
    def init(self,x,p):
        super().init(a=x.min(), b=x.max())
        self.x = x
        self.p = p
def _pdf(self,x):
    return sp.interpolate.interp1d(self.x,self.p)(x)


x = np.linspace(0,1,101)
f = 3x*2
my_cv = my_pdf(x,f)
my_cv.pdf(0)
my_cv.cdf(0.5)

Still, I feel like there should be a package for operations on this kind of random variables?

Define continuous, non-analytical pdfs in python

1 Answers1