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Probabilistic programming (PP) is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically.[1] It represents an attempt to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable.[2][3] It can be used to create systems that help make decisions in the face of uncertainty.
Programming languages used for probabilistic programming are referred to as "probabilistic programming languages" (PPLs).
^"Probabilistic programming does in 50 lines of code what used to take thousands". phys.org. April 13, 2015. Retrieved April 13, 2015.
^"Probabilistic Programming". probabilistic-programming.org. Archived from the original on January 10, 2016. Retrieved December 24, 2013.
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is an open source Julia library for Bayesian Inference using probabilisticprogramming. Geman, S.; Geman, D. (1984). "Stochastic Relaxation, Gibbs Distributions...
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