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From "Phil Steitz (JIRA)" <>
Subject [jira] Commented: (MATH-167) ConvergenceException in normal CDF
Date Sat, 07 Jul 2007 15:37:04 GMT


Phil Steitz commented on MATH-167:

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:
>             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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