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From "Mikkel Meyer Andersen (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (MATH-585) Very slow generation of gamma random variates
Date Thu, 09 Jun 2011 20:00:59 GMT

     [ https://issues.apache.org/jira/browse/MATH-585?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Mikkel Meyer Andersen updated MATH-585:
---------------------------------------

    Attachment: MATH585-1.patch

This is _NOT_ a final patch proposal!

This is partly for experimenting and validation purposes (the old inversion based nextGamma
is renamed to nextOldGamma), and partly for showing how I solved the many constants and caching
involved in the algorithms (see the new class GammaRejectionSampler - I'm not sure what how
I myself think about that way of doing it, maybe a private class in RandomDataImpl is better?).

So please comment on those two subjects.

> Very slow generation of gamma random variates
> ---------------------------------------------
>
>                 Key: MATH-585
>                 URL: https://issues.apache.org/jira/browse/MATH-585
>             Project: Commons Math
>          Issue Type: Improvement
>    Affects Versions: 2.2, 3.0
>         Environment: All
>            Reporter: Darren Wilkinson
>            Assignee: Mikkel Meyer Andersen
>              Labels: Gamma, Random
>         Attachments: MATH585-1.patch
>
>   Original Estimate: 6h
>  Remaining Estimate: 6h
>
> The current implementation of gamma random variate generation works, but uses an inversion
method. This is well-known to be a bad idea. Usually a carefully constructed rejection procedure
is used. To give an idea of the magnitude of the problem, the Gamma variate generation in
Parallel COLT is roughly 50 times faster than in Commons Math. 

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