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https://issues.apache.org/jira/browse/RNG-195?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Alex Herbert updated RNG-195:
-----------------------------
Affects Version/s: 1.4
(was: 1.3)
> DirichletSampler has unbounded recursion and stack overflow for small alpha
> parameters
> --------------------------------------------------------------------------------------
>
> Key: RNG-195
> URL: https://issues.apache.org/jira/browse/RNG-195
> Project: Commons RNG
> Issue Type: Bug
> Components: sampling
> Affects Versions: 1.4
> Reporter: Alex Herbert
> Assignee: Alex Herbert
> Priority: Minor
> Fix For: 1.8
>
>
> The DirichletSampler uses a rejection method to sample. A gamma sample is
> obtained for each concentration parameter alpha. The vector of k samples is
> normalised to unit length. When the sum of the samples is zero or infinite
> the sample cannot be normalised and recursion occurs to generate another
> sample.
> If the alpha parameters are very small the gamma samples are always zero and
> a stack overflow error occurs.
> Note that recursion is expected to be used to indicate a non-functional RNG
> underlying the sampler. It was not the original intention to use a stack
> overflow error to indicate a badly parameterised sampler where samples are
> impossible.
> Issue identified using a security scan.
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