# [Python-Dev] Fw: random.py gives wrong results (+ a solution)

Fredrik Lundh fredrik@effbot.org
Thu, 25 Jan 2001 20:23:50 +0100

```I'm pretty sure Tim's seen this already, but just
in case...

----- Original Message -----
From: "Ivan Frohne" <frohne@gci.net>
Newsgroups: comp.lang.python
Sent: Thursday, January 25, 2001 5:20 PM
Subject: Re: random.py gives wrong results (+ a solution)

>
> "Janne Sinkkonen" <janne@oops.nnets.fi> wrote in message
> news:m3u26oy1rw.fsf@kinos.nnets.fi...
> >
> > At least in Python 2.0 and earlier, the samples returned by the
> > function betavariate() of random.py are not from a beta distribution
> > although the function name misleadingly suggests so.
> >
> > The following would give beta-distributed samples:
> >
> > def betavariate(alpha, beta):
> >      y = gammavariate(alpha,1)
> >      if y==0: return 0.0
> >      else: return  y/(y+gammavariate(beta,1))
> >
> > This is from matlab. A comment in the original matlab code refers to
> > Devroye, L. (1986) Non-Uniform Random Variate Generation, theorem 4.1A
> > (p. 430). Another reference would be Gelman, A. et al. (1995) Bayesian
> > data analysis, p. 481, which I have checked and found to agree with
> > the code above.
>
>
> I'm convinced that Janne Sinkkonen is right:  The beta distribution
> generator in module random.py does not return Beta-distributed
> random numbers.  Janne's suggested fix should work just fine.
>
> Here's my guess on how and why this bug bit  -- it won't be of interest to
> most but
> this subject is so obscure sometimes that there needs to be a detailed
> analysis.
>
> The probability density function of the gamma distribution with (positive)
> parameters
> A and B is usually written
>
>     g(x; A, B) = (x**(A-1) * exp(x/B)) / (Gamma(A) * B**A), where x, A, and
> B > 0.
>
> Here Gamma(A) is the gamma function -- for A a positive integer, Gamma(A) is
> the
> factorial of A - 1, Gamma(A) = (A-1)!.  In fact, this is the definition used
> by the authors of random.py in defining gammavariate(alpha, beta), the gamma
> distribution random number generator.
>
> Now it happens that a gamma-distributed random variable with parameters A =
> 1 and
> B has the (much simpler) exponential distribution with density function
>
>     g(x; 1, B) = exp(-x/B) / B.
>
> Keep that in mind.
>
> The reference "Discrete Event Simulation in ," by Kevin Watkins
> (McGraw-Hill, 1993)
> was consulted by the random.py authors.  But this reference defines the
> gamma probability distribution a little differently, as
>
>     g1(x; A, B) =  (B**A * x**(A-1) * exp(B*x)) / Gamma(A), where x, A, B >
> 0.
>
> (See p. 85).  On page 87, Watkins states (incorrectly) that if grv(A, B) is
> a function which
> returns a gamma random variable with parameters A and B (using his
> definition on p. 85),
> then the function
>
>     brv(A, B) = grv(1, 1/B) / ( grv(1, 1/B) + grv(1, A) )              [ not
> true!]
>
> will return a random variable which has the beta distribution with
> parameters A and B.
>
> Believing Watkins to be correct, the random.py authors remembered that a
> gamma
> random variable with parameter A = 1 is just an exponential random variable
> and
> further simplified their beta generator to
>
>    brv(A, B) = erv(1/B) / (erv(1/B) + erv(A)), where erv(K) is a random
> variable
>
> having the exponential distribution with
>
> parameter K.
>
> The corrected equation for a beta random variable, using Watkins' definition
> of the
> gamma density, is
>
>     brv(A, B) = grv(A, 1) / ( grv(A, 1) + grv(1/B, 1) ),
>
> which translates to
>
>     brv(A, B) = grv(A, 1) / (grv(A, 1) + grv(B, 1)
>
> using the more common gamma density definition (the one used in random.py).
> Many standard statistical references give this equation -- two are
> "Non-Uniform random Variate Generation," by Luc Devroye, Springer-Verlag,
> 1986,
> p. 432, and  "Monte Carlo Concepts, Algorithms and Applications," by
> George S. Fishman, Springer, 1996, p. 200.
>
> --Ivan Frohne
>
>
>
>
> >>>
>
>
>
>

```