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https://issues.apache.org/jira/browse/STATISTICS-35?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Alex Herbert updated STATISTICS-35:
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Attachment: normal_approx_delta.jpg
> PoissonDistribution cannot create a sampler with a mean above 2^30
> ------------------------------------------------------------------
>
> Key: STATISTICS-35
> URL: https://issues.apache.org/jira/browse/STATISTICS-35
> Project: Apache Commons Statistics
> Issue Type: Bug
> Components: distribution
> Affects Versions: 1.0
> Reporter: Alex Herbert
> Priority: Minor
> Attachments: normal_approx_delta.jpg
>
>
> The PoissonDistribution can be parameterised with any mean and yet the
> PoissonSampler in RNG excludes a mean above 2^30 to avoid truncation. This
> change was made to avoid distribution truncation and because the algorithm
> uses (int) floor(mean) (see [RNG-52]).
> Commons RNG now has a LongSampler interface and it may be possible to create
> a PoissonSampler that outputs a long with the same algorithm and avoid
> truncation to int values. The final PoissonDistribution is still bounded by
> int values but the sampler can be less restrictive if it uses long. At large
> means the Poisson sampler mainly uses a Gaussian
> sampler and runtime performance should not be impacted.
> Investigate if the PoissonSampler algorithm can create samples from the
> PoissonDistribution that passes a chi-square test when the mean is large. If
> this is possible then implement this in RNG. Otherwise inverse transform
> sampling will have to be used for large mean (with an anticipated large
> performance impact).
>
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