https://github.com/shawnlaffan/perl-statistics-sampler-multinomial

Multinomial sampler for Perl
https://github.com/shawnlaffan/perl-statistics-sampler-multinomial

hacktoberfest multinomial perl prng statistics

Last synced: 24 days ago
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Multinomial sampler for Perl

• Host: GitHub
• URL: https://github.com/shawnlaffan/perl-statistics-sampler-multinomial
• Owner: shawnlaffan
• Created: 2016-12-30T08:56:16.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2022-09-24T09:56:49.000Z (over 2 years ago)
• Last Synced: 2024-11-14T04:58:51.253Z (3 months ago)
• Topics: hacktoberfest, multinomial, perl, prng, statistics
• Language: Perl
• Homepage:
• Size: 146 KB
• Stars: 2
• Watchers: 3
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: Changes

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README

        
# perl-statistics-sampler-multinomial

Implements multinomial sampling using two methods, the

conditional binomial method, and Vose's version of the alias method

(as a separate package).

The setup time for the alias method is longer than for other methods,

and the memory requirements are larger since it maintains two lists in memory,

but this is amortised when 

when generating repeated samples because only two random numbers are

needed for each draw, as compared to up to O(log n) for other methods.

This should have a pay off when, for example calculating 

bootstrap confidence intervals for a set of classes.

However, benchmarking indicates that the conditional binomial code is

substantially (approximately four times) faster than the alias method code

when using the Mersenne Twister implemented in Math::Random::MT::Auto.

Profiling using Devel::NYTProf indicates this is probably due to

Perl level overheads for the PRNG method calls in the Alias code.

For the conditional binomial code most of the PRNG calls are

at the C level inside XS code.

Benchmarking also indicates that the Math::GSL::Randist implementation

is a few orders of magnitude faster still, so if you aren't worried about

using your own PRNG, and can install Math::GSL::Randist,

then you should consider using that.

The Math::Random implementation is also faster than this module,

but not as fast as the GSL.

So if it is not faster, then why would you use this module?

The main reason is that it allows you to pass your own

PRNG object, and thus you can continue sampling from an existing PRNG stream

within your analysis using the PRNG of your choice.

This simplifies reproducibility of results as one only

needs to store the starting state for one PRNG, not several.

For more details and background about multinomial sampling,

see http://www.keithschwarz.com/darts-dice-coins

## COPYRIGHT AND LICENCE

Copyright (C) 2016, Shawn Laffan

This library is free software; you can redistribute it and/or modify

it under the same terms as Perl itself.