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https://issues.apache.org/jira/browse/MATH-282?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Phil Steitz updated MATH-282:
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Attachment: distributions.patch
The attached patch resolves this issue as well as MATH-301 and the problems
described above with the Poisson distribution. The patch bundles quite a few
changes, some of which are not strictly necessary to resolve these issues. The
following is a summary.
* BrentSolver has been changed to expose its configured absolute accuracy.
This solver is used by the default inverse cum implementation in
AbstractContinuousDistribution and the hard-coded setting (1E-6) was limiting
accuracy in inverse cumulative probability estimates.
AbstractContinuousDistribution was changed to allow distributions to set this
value and NormalDistributionImpl was changed to set it to 1E-9 by default and
allow users to configure it via a constructor argument. If all are happy with
this change, I will similarly change other distributions to override the new
getSolverAbsoluteAccuracy method and add constructors to configure the value.
* AbstractContinuousDistribution and AbstractIntegerDistribution
inverseCumulativeProbability methods have been modified to check for NaN values
returned by cumulativeProbability and throw MathExceptions when this happens.
* The criteria for choosing between the Lanczos series and continued fraction
expansion when computing regularized gamma functions has been changed to (x >=
a + 1). When using the series approximation (regularizedGammaP), divergence to
infinity is checked and when this happens, 1 is returned.
* When scaling continued fractions to (try to) avoid divergence to infinity,
the larger of a and b is used as a scale factor and the attempt to scale is
repeated up to 5 times, using successive powers of the scale factor.
* The maximum number of iterations used in estimating cumulative probabilities
for PoissonDistributionImpl has been decreased from Integer.MAX_VALUE to
10000000 and made configurable.
Review and comment much appreciated. One thing that I would like improve is to
get decent top-coding in place in terms of the arguments to the regularized
gamma functions. The Poisson inverse cum tests take a very long time now
because for very large values of x, the continued fractions are taking a long
time to converge. This is needless computation, as the value returned is 1.
We should be able to analytically determine bounds here.
> ChiSquaredDistributionImpl.cumulativeProbability > 1
> ----------------------------------------------------
>
> Key: MATH-282
> URL: https://issues.apache.org/jira/browse/MATH-282
> Project: Commons Math
> Issue Type: Bug
> Affects Versions: 1.0, 1.1, 1.2, 2.0
> Environment: called from Scala code
> Reporter: Adam Kiezun
> Assignee: Phil Steitz
> Fix For: 2.1
>
> Attachments: distributions.patch, math-282.patch
>
>
> Calling
> new ChiSquaredDistributionImpl(1.0).cumulativeProbability(66.41528551683048)
> returns 1.000000000000004, which is bogus (should never be > 1)
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