Probabilistic (logic) programming concepts

doi:10.1007/s10994-015-5494-z

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Probabilistic (logic) programming concepts

2015-05-08

137

Luc De Raedt

Angelika Kimmig *

DOI：10.1007/s10994-015-5494-z

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Abstract

En 中文

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.

Keywords:

Probabilistic programming languages

Probabilistic logic programming

Statistical relational learning

Inference in probabilistic languages

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