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https://issues.apache.org/jira/browse/MATH-167?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#action_12510905
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Phil Steitz commented on MATH-167:
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Thanks for reporting this. I see three alternatives to address - appreciate
comments.
1) Determine tail resolution possible with current impl (hopefully not
different on different JDKs, platforms) and "top code", checking arguments and
returning 0 or 1, resp if argument is too far in SD units from the mean. To
find the cut points, empirically determine where convergence starts to fail.
Document the cut points in javadoc for Impl.
2) Catch ConvergenceException and return 0 or 1, resp if argument is far from
the mean; rethrow otherwise (though this should never happen).
3) Resolve as WONTFIX and leave it to client to catch and handle
ConvergenceException, examining argument. Document algorithm more fully and
warn that ConvergenceException will be thrown if tail probability cannot be
accurately estimated or distinguished from 0.
My first thought was 2 and I guess I still favor that, since 3) is inconvenient
for users and 1) may not be stable unless cut points are conservative.
Note that this same problem may apply to tail probablilities of other
continuous distributions and we should check and address all of these before
resolving this issue.
> ConvergenceException in normal CDF
> ----------------------------------
>
> Key: MATH-167
> URL: https://issues.apache.org/jira/browse/MATH-167
> Project: Commons Math
> Issue Type: Bug
> Reporter: Mikko Kauppila
> Priority: Minor
>
> NormalDistributionImpl::cumulativeProbability(double x) throws
> ConvergenceException
> if x deviates too much from the mean. For example, when x=+/-100, mean=0,
> sd=1.
> Of course the value of the CDF is hard to evaluate in these cases,
> but effectively it should be either zero or one.
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