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From Sébastien Brisard (Commented) (JIRA) <>
Subject [jira] [Commented] (MATH-731) Triangular distribution
Date Thu, 12 Jan 2012 07:05:39 GMT


Sébastien Brisard commented on MATH-731:

Patch committed in {{r1230419}}, with the following changes
* Removed {{getLowerLimit()}} and {{getUpperLimit()}}, which are redundant with {{getSupportLowerBound()}}
and {{getSupportUpperBound()}} (the Javadoc is explicit enough about that).
* Implemented explicit formulas for {{inverseCumulativeProbability(double)}}.
* Removed DEFAULT_INVERSE_ABSOLUTE_ACCURACY, which has now become meaningless (since {{inverseCumulativeProbability(double)}}
no longer resorts to an iterative solver). I guess {{getSolverAbsoluteAccuracy()}} should
return a reliable estimate on the absolute accuracy of the explicit calculations performed
in {{inverseCumulativeProbability(double)}}. As I have not a clear view on what this accuracy
is, I've {color:red}arbitrarily{color} initialized {{solverAbsoluteAccuracy}} with {{FastMath.ulp(c)}}.
It should probably rather be something like {{s * FastMath.ulp(c)}}, with {{s > 1}} depending
on {{a}}, {{b}} and {{c}}. {color:red}Thoughts?{color}
* Used default implementation of {{sample()}} instead of proposed version, as they are *exactly*
identical (see explicit implementation of {{inverseCumulativeProbability(double)}}.
* Removed references to Ivan Frohnes' code.
* Updated serialVersionUID

I'll leave this issue unresolved until we come to an agreement on the accuracy issue.

Thanks for this contribution!

Afterthoughts: we should probably implement explicit expressions as well for the inverse cumulative
probability of a uniform distribution.
> Triangular distribution
> -----------------------
>                 Key: MATH-731
>                 URL:
>             Project: Commons Math
>          Issue Type: New Feature
>            Reporter: Dennis Hendriks
>            Assignee: Sébastien Brisard
>              Labels: distribution
>             Fix For: 3.0
>         Attachments: triangular_distribution.patch
> It would be nice to have a triangular distribution.

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