Probabilistic (logic) programming concepts

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 particu...

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Veröffentlicht in:Machine learning Jg. 100; H. 1; S. 5 - 47
Hauptverfasser: De Raedt, Luc, Kimmig, Angelika
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2015
Springer Nature B.V
Schlagworte:
ISSN:0885-6125, 1573-0565, 1573-0565
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:0885-6125
1573-0565
1573-0565
DOI:10.1007/s10994-015-5494-z