Title: Probabilistic (logic) programming concepts
Authors: De Raedt, Luc
Kimmig, Angelika # ×
Issue Date: 2015
Publisher: Springer New York LLC
Series Title: Machine Learning vol:100 issue:1 pages:5-47
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.
ISSN: 0885-6125
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
deraedt_kimmig_mlj15.pdf Published 517KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science