De Raedt, Luc and Kimmig, Angelika ![]() |
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Abstract
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 |
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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: | 19 Nov 2024 23:45 |
URI: | https://orca.cardiff.ac.uk/id/eprint/106737 |
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