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Alex Herbert commented on STATISTICS-31: ---------------------------------------- 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); > } > ``` > -- This message was sent by Atlassian Jira (v8.3.4#803005)