Probabilistic (logic) programming concepts
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| 003 | CHVBK | ||
| 005 | 20210128100404.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150701xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-015-5494-z |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-015-5494-z | ||
| 245 | 0 | 0 | |a Probabilistic (logic) programming concepts |h [Elektronische Daten] |c [Luc De Raedt, Angelika Kimmig] |
| 520 | 3 | |a 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. | |
| 540 | |a The Author(s), 2015 | ||
| 690 | 7 | |a Probabilistic programming languages |2 nationallicence | |
| 690 | 7 | |a Probabilistic logic programming |2 nationallicence | |
| 690 | 7 | |a Statistical relational learning |2 nationallicence | |
| 690 | 7 | |a Inference in probabilistic languages |2 nationallicence | |
| 700 | 1 | |a De Raedt |D Luc |u Department of Computer Science, KU Leuven, Celestijnenlaan 200A, Bus 2402, 3001, Heverlee, Belgium |4 aut | |
| 700 | 1 | |a Kimmig |D Angelika |u Department of Computer Science, KU Leuven, Celestijnenlaan 200A, Bus 2402, 3001, Heverlee, Belgium |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/1(2015-07-01), 5-47 |x 0885-6125 |q 100:1<5 |1 2015 |2 100 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-015-5494-z |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-015-5494-z |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a De Raedt |D Luc |u Department of Computer Science, KU Leuven, Celestijnenlaan 200A, Bus 2402, 3001, Heverlee, Belgium |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Kimmig |D Angelika |u Department of Computer Science, KU Leuven, Celestijnenlaan 200A, Bus 2402, 3001, Heverlee, Belgium |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/1(2015-07-01), 5-47 |x 0885-6125 |q 100:1<5 |1 2015 |2 100 |o 10994 | ||