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https://issues.apache.org/jira/browse/STATISTICS-31?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17382322#comment-17382322
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Alex Herbert edited comment on STATISTICS-31 at 7/16/21, 8:27 PM:
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This should also apply to the DiscreteDistribution. Of the implementations the
following marked with a * can have a high precision or alternate implementation
for the survival function:
* Binomial *
* Geometric *
* Hypergeometric *
* Pascal
* Poisson *
* Uniform
* Zipf
The Hypergeometric even has a method to do this called
{{upperCumulativeProbability}}.
[~benwtrent] does Wolfram have survival probability for these distributions
where we can obtain test data?
was (Author: alexherbert):
This should also apply to the DiscreteDistribution. Of the implementations the
following marked with a * can have a high precision or alternate implementation
for the survival function:
* Binomial *
* Geometric *
* Hypergeometric *
* Pascal
* Poisson *
* Uniform *
* Zipf
The Hypergeometric even has a method to do this called
{{upperCumulativeProbability}}.
[~benwtrent] does Wolfram have survival probability for these distributions
where we can obtain test data?
> Add survival probability function to continuous distributions
> -------------------------------------------------------------
>
> Key: STATISTICS-31
> URL: https://issues.apache.org/jira/browse/STATISTICS-31
> Project: Apache Commons Statistics
> Issue Type: New Feature
> Reporter: Benjamin W Trent
> Priority: Major
> Time Spent: 40m
> Remaining Estimate: 0h
>
> It is useful to know the [survival
> function|[https://en.wikipedia.org/wiki/Survival_function]] of a number given
> a continuous distribution.
> While this can be approximated with
> {noformat}
> 1 - cdf(x){noformat}
> , there is an opportunity for greater accuracy in certain distributions.
>
> A good example of this is the gamma distribution. The survival function for
> that distribution would probably look similar to:
>
> ```java
> @Override
> public double survivalProbability(double x) {
> if (x <= SUPPORT_LO)
> { return 1; }
> else if (x >= SUPPORT_HI)
> { return 0; }
> return RegularizedGamma.Q.value(shape, x / scale);
> }
> ```
>
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