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 |
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| Hauptverfasser: | , |
| 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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| Bibliographie: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0885-6125 1573-0565 1573-0565 |
| DOI: | 10.1007/s10994-015-5494-z |