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Probabilistic (logic) programming concepts

De Raedt, Luc and Kimmig, Angelika 2015. Probabilistic (logic) programming concepts. Machine Learning 100 (1) , pp. 5-47. 10.1007/s10994-015-5494-z

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A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer Verlag (Germany)
ISSN: 0885-6125
Date of First Compliant Deposit: 20 November 2017
Date of Acceptance: 17 March 2015
Last Modified: 07 Dec 2020 18:38

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