[Python-Dev] Fw: random.py gives wrong results (+ a solution)
Thu, 25 Jan 2001 20:23:50 +0100
I'm pretty sure Tim's seen this already, but just
----- Original Message -----
From: "Ivan Frohne" <firstname.lastname@example.org>
Sent: Thursday, January 25, 2001 5:20 PM
Subject: Re: random.py gives wrong results (+ a solution)
> "Janne Sinkkonen" <email@example.com> wrote in message
> > 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
> The probability density function of the gamma distribution with (positive)
> 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
> 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 >
> (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
> 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
> random variable with parameter A = 1 is just an exponential random variable
> further simplified their beta generator to
> brv(A, B) = erv(1/B) / (erv(1/B) + erv(A)), where erv(K) is a random
> 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,
> p. 432, and "Monte Carlo Concepts, Algorithms and Applications," by
> George S. Fishman, Springer, 1996, p. 200.
> --Ivan Frohne