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https://issues.apache.org/jira/browse/MATH-460?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13464189#comment-13464189
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Phil Steitz commented on MATH-460:
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The test is doing a crude verification that the distribution of values
generated by the sample() method is consistent with the distribution function.
If the quartiles are correct and the sample() method is functioning correctly,
generated values should be evenly divided among quartile ranges. The Chisquare
test verifies that. When it fails, it should show you what the bin counts are
and you can see where too many or too few values are being generated. You can
also check the quartiles themselves using R's qlevy(p,m,s) function. Compare
what R gives for p=0.25, 0.5, 0.75 to what your code gives for these quartiles.
And yes, R quantile tests would be helpful. Patches welcome :)
> Levy Distribution
> -----------------
>
> Key: MATH-460
> URL: https://issues.apache.org/jira/browse/MATH-460
> Project: Commons Math
> Issue Type: New Feature
> Reporter: Pavel Ryzhov
> Priority: Minor
> Fix For: 3.2
>
> Attachments: levy_math_460.patch
>
>
> Pretty straightforward implementation of Levy Distribution (not Levy
> alpha-stable) according to http://en.wikipedia.org/wiki/Lévy_distribution.
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