Anyone attempting to generate non-uniform random numbers should consult "Non-Uniform Random Variate Generation" by Luc Devroye, which is available in full, for free use at: http://cg.scs.carleton.ca/~luc/rnbookindex.html Poisson generation is covered, specifically, in chapter 10: http://cg.scs.carleton.ca/~luc/chapter_ten.pdf starting on page 501. How far off is the Poisson distribution from the normal at a mean of 750? How large a sample would you need to have, say, a one in a thousand chance of detecting the difference? (I don't know the answer, but if there is a problem at that extreme, its a question that should be answered before getting too fancy with "precise" solutions). Topher At 07:08 AM 12/24/2007, you wrote:
-----Original Message----- Sent: 22 December 2007 23:02 To: boost@lists.boost.org Subject: [boost] [random] poisson_distribution problem and fix
The poisson_distribution code will fail for any mean larger than 750 or so (on my x86_64 machine). The problem is a comparison with exp(-_mean) which evaluates as zero right around there.
A far better fix would be to use an algorithm other than Knuth's for this. Knuth's algorithm is simple, but O(n). Here is some code I wrote, converting some old Fortran code that a friend had got from Numerical Recipes once upon a time. It works, but would need actual testing before use in production code:
#ifndef F_POISSON_HEADER #define F_POISSON_HEADER
<snipped>
#endif
This looks too obviously lifted from NR in C (or FORTRAN) to fit the Boost license.
. . .
Paul
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