Scaling exact inference for discrete probabilistic programs

Probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge of probabilistic inference remains the primary roadblock for applying PPLs in practice. Inference is fundamentally hard, so there is no one-size-fi...

Full description

Bibliographic Details

Published in:	Proceedings of ACM on programming languages Vol. 4; no. OOPSLA; pp. 1 - 31
Main Authors:	Holtzen, Steven, Van den Broeck, Guy, Millstein, Todd
Format:	Journal Article
Language:	English
Published:	New York, NY, USA ACM 13.11.2020
Subjects:	Mathematics of computing Probabilistic inference problems Probabilistic representations Probability and statistics Probabilistic programming
ISSN:	2475-1421, 2475-1421
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!