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== Software implementations ==
There are software implementations available for applying the inverse sampling method by using numerical approximations of the inverse in the case that it is not available in closed form. For example, an approximation of the inverse can be computed if the user provides some information about the distributions such as the PDF <ref>
{{cite journal | last1 = Derflinger | first1 = Gerhard | last2 = Hörmann | first2 = Wolfgang | last3 = Leydold | first3 = Josef | title = Random variate generation by numerical inversion when only the density is known | journal = ACM Transactions on Modeling and Computer Simulation | date = 2010 | volume = 20 | issue = 4 | doi = 10.1145/945511.945517| url = https://epub.wu.ac.at/664/1/document.pdf }}</ref> or the CDF.
* C library UNU.RAN <ref>{{cite web | url=https://statmath.wu.ac.at/unuran/index.html | title=UNU.RAN - Universal Non-Uniform RANdom number generators }}</ref>
* R library Runuran <ref>{{cite web | url=https://cran.r-project.org/package=Runuran | title=Runuran: R Interface to the 'UNU.RAN' Random Variate Generators | date=17 January 2023 }}</ref>
* Python subpackage sampling in [[SciPy|scipy.stats]]<ref>{{cite web | url=https://docs.scipy.org/doc/scipy/reference/stats.sampling.html | title=Random Number Generators (Scipy.stats.sampling) — SciPy v1.12.0 Manual }}</ref><ref>
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== See also ==
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