FYI: Something I saw on generating random-numbers
- From: dkcombs@xxxxxxxxx (David Combs)
- Date: 23 Sep 2006 21:57:32 -0400
I like to lurk over at comp.graphics.algorithms -- extremely
good math stuff, and explanations, there.
(Not that I understand any of it!)
Especially long posts by this guy/gal who goes by "d'FAQS".
Since generation of random numbers is often talked about
here in this group, for those who like that kind of stuff,
[even you likely know most if not all of these methods]
I post this (and which I just saw):
From nobody-here@xxxxxxx Sat Sep 23 21:49:00 EDT 2006Article: 165648 of comp.graphics.algorithms
NNTP-Posting-Date: Sat, 02 Sep 2006 20:02:38 -0500
From: Just d' FAQs <nobody-here@xxxxxxx>
Newsgroups: comp.graphics.algorithms
Subject: Re: Stochastic Positioning of A Point in A 3D Gaussian Distribution
Xref: panix comp.graphics.algorithms:165648
On 1 Sep 2006 09:56:43 -0700, hoffmann@xxxxxxxxxxxx wrote:
1. Why is the Box-Muller Transform, which converts two PRNG
(pseudo random number generator) numbers u1,u2 with
uniform distributions into a geometrical pair x,y with Gaussian
distributions, better than a transform which converts simply
one PRNG number with uniform distribution into one number
with Gaussian distribution ?
http://en.wikipedia.org/wiki/Box-Muller_transform
The OP needs obviously THREE numbers for x,y,z.
2. Numbers by a PRNG with uniform distribution can be trans-
formed into numbers with Gaussian distribution either by adding
N numbers or by averaging N numbers.
E.g. one can find that N=6 is reasonable and N=12 is near to
perfect.
IMO this is perhaps faster than using Box-Muller.
Is any comparison of quality available ?
There is no reason anyone should need to write their own pseudo-random
number generator, whether for uniform or Gaussian distributions. It's
really easy to create a bad uniform generator, and even the venerable
Box-Muller generator for Gaussians has two major variations, one of
which is troublesome.
Four possibilities are:
* Box-Muller: this gives two independent variates for each call, and
is still fast even if one is discarded.
* Ratio: This is a method using rejection, and the ellipse regions of
Leva make it fast as well as brief.
* Averaging uniform: This is both slow and inaccurate, it is not to
be recommended even with some known adjustments.
* Ziggurat: A variation of the "rectangle-wedge-tail" approach, this
tends to be the fastest, but requires a large table.
Two good sources to consult are Luc Devroye,
<http://cg.scs.carleton.ca/~luc/rng.html>
(including his book, a free download),
<http://cg.scs.carleton.ca/~luc/books-luc.html>
and Knuth,
Knuth, D. The Art of Computer Programming, Vol. II., 3/e.
Or just grab an implementation, such as winrand,
<http://crypto.mat.sbg.ac.at/ftp/pub/data/winrand.zip>
or the GNU Scientific Library (GSL).
<http://www.gnu.org/software/gsl/>
I hope it turns out of interest.
David
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