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https://issues.apache.org/jira/browse/STATISTICS-25?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17220398#comment-17220398
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Gilles Sadowski commented on STATISTICS-25:
-------------------------------------------
Thanks. Note that in the description you mixed
* {{cumulativeProbablility}} and
* {{inverseCumulativeProbability}}.
The list clarifies the actual issue.
This is the output of the code currently in "Commons Statistics":
{noformat}
0.5079560899120266
0.5097265951015977
0.5099476080930287
0.5099700243396535
0.5099724933430037
0.5099641022995844
1.0
1.0
{noformat}
Introducing a change based on the info on
[Wikipedia|https://en.wikipedia.org/wiki/Student%27s_t-distribution] (normal
distribution for very large number of degrees of freedom), the output will
become
{noformat}
0.5079560899120266
0.5097265951015977
0.5099476080930287
0.5099700243396535
0.5099724933430037
0.509972518195238
0.509972518195238
0.509972518195238
{noformat}
> 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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