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https://issues.apache.org/jira/browse/STATISTICS-25?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17220408#comment-17220408
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Andreas Stefik commented on STATISTICS-25:
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Oh crud, you're right. My mistake! I definitely "meant" the CDF function, not
its inverse.
In any case, good catch. So, basically, it goes to ERF as it approaches
infinity. Wikipedia doesn't say, but any sense what the cutoff should be? Glad
it's not a terribly difficult change to fix it!
> T Distribution Inverse Cumulative Probability Function gives the Wrong Answer
> -----------------------------------------------------------------------------
>
> Key: STATISTICS-25
> URL: https://issues.apache.org/jira/browse/STATISTICS-25
> Project: Apache Commons Statistics
> Issue Type: Bug
> Reporter: Andreas Stefik
> Priority: Major
>
> Hi There,
> Given code like this:
>
> import org.apache.commons.math3.analysis.UnivariateFunction;
> import org.apache.commons.math3.analysis.solvers.BrentSolver;
> import org.apache.commons.math3.distribution.TDistribution;
> public class Main {
> public static void main(String[] args) {
> double df = 1E38;
> double t = 0.975;
> TDistribution dist = new TDistribution(df);
>
> double prob = dist.inverseCumulativeProbability(1.0 - t);
>
> System.out.println("Prob: " + prob);
> }
> }
>
> It is possible I am misunderstanding, but that seems equivalent to:
>
> scipy.stats.t.cdf(1.0 - 0.975, 1e38)
>
> In Python. They give different answers. Python gives 0.509972518193, which
> seems correct, whereas Apache Commons gives Prob: -6.462184036284304E-10.
> That's a huge difference.
> My hunch is that as you get closer to infinity it begins to fail, but I
> haven't checked carefully. For calls with much smaller degrees of freedom,
> you get answers that are basically the same as Python or online calculators.
>
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