[Python-bugs-list] [Bug #130030] Claim of bad betavariate algorithm
Thu, 25 Jan 2001 22:19:36 -0800
Bug #130030, was updated on 2001-Jan-25 03:03
Here is a current snapshot of the bug.
Category: Python Library
Bug Group: None
Submitted by: tim_one
Assigned to : tim_one
Summary: Claim of bad betavariate algorithm
Details: From c.l.py. Beats me, but sounds credible. random.py cites
Discrete Event Simulation in C, pp 87-88, for its algorithm.
[Janne Sinkkonen (mailto:firstname.lastname@example.org)]
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
Date: 2001-Jan-25 22:19
c.l.py post by Ivan Frohne:
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
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
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,
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 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
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.
Date: 2001-Jan-25 13:36
I have no idea why the Group was set to Irreproducible, but have seen that
on other recent new bug reports too -- maybe a new SF buglet.
Anyway, changed group to None. Ivan Frohne helpfully investigated this,
and made what looks to be a very strong case for adopting the algorithm
For detailed info, follow this link: