[
https://issues.apache.org/jira/browse/NUMBERS-174?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17441457#comment-17441457
]
Gilles Sadowski commented on NUMBERS-174:
-----------------------------------------
bq. Having used both I prefer the generator implementation. But it adds another
implementation to maintain.
Hence, I'd favour option 4 (and deprecate the current implementation).
> Update Gamma functions using the Boost implementation
> -----------------------------------------------------
>
> Key: NUMBERS-174
> URL: https://issues.apache.org/jira/browse/NUMBERS-174
> Project: Commons Numbers
> Issue Type: Improvement
> Components: gamma
> Affects Versions: 1.0
> Reporter: Alex Herbert
> Assignee: Alex Herbert
> Priority: Major
> Fix For: 1.1
>
>
> The current regularised incomplete gamma functions for P and Q compute using
> a series representation for all input. This method is not robust to extreme
> arguments.
> The Gamma functions are used in Commons Statistics in the Gamma and Poisson
> distributions.
> Large values of the mean in the Poisson distribution have low
> precision for the CDF. The distribution also has a configurable
> threshold for convergence of the gamma function evaluation
> (STATISTICS-38). Ideally this implementation detail should be
> removed from the API.
> The Gamma distribution has a function switch based on a threshold to
> compute the PDF. This threshold results in incorrect values for
> certain parameters (STATISTICS-39).
> The function switch in the Gamma distribution is based on the
> documentation for the Boost gamma functions [1]. However it does not
> implement all the suggested optimisations detailed in the most recent
> Boost documentation. This includes avoiding using the converging
> series of the gamma function when the convergence is slow or unstable,
> i.e. addresses the need for a configurable convergence threshold in
> the Poisson distribution.
> One part of the evaluation of the incomplete gamma functions require
> accuracy in the leading term:
> {noformat}
> x^a exp(-x) / gamma(a) = exp(a log(x) - x - loggamma(a){noformat}
> When x and a are large then using logs to compute this leads to
> cancellation. Use of logs to compute this term is done in the current
> implementation in numbers for RegularizedGamma P and Q. It is the
> source of low precision for the CDF for the Poisson distribution when
> the mean is large.
> I propose to port the Boost gamma functions to numbers. This will be a
> process similar to the recent port of the error functions to the same
> package in numbers. These functions improved both accuracy and
> speed over the existing implementation. Once the gamma functions are
> ported a comparison can be made between the existing and new
> implementations.
> [1]
> [https://www.boost.org/doc/libs/1_77_0/libs/math/doc/html/math_toolkit/sf_gamma/igamma.html]
--
This message was sent by Atlassian Jira
(v8.20.1#820001)
