Probabilistic (logic) programming concepts
Probabilistic (logic) programming concepts
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DOI：
10.1007/s10994-015-5494-z
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发表时间：
2015-07-01
期刊：
影响因子：
7.5
通讯作者：
Kimmig, Angelika
中科院分区：
文献类型：
--
作者：
De Raedt, Luc;Kimmig, Angelika
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.
