Title: The magic of logical inference in probabilistic programming
Authors: Gutmann, Bernd ×
Thon, Ingo
Kimmig, Angelika
Bruynooghe, Maurice
De Raedt, Luc #
Issue Date: 6-Jul-2011
Publisher: Cambridge University Press
Series Title: Theory and Practice of Logic Programming vol:11 pages:663-680
Conference: International Conference on Logic Programming edition:27 location:Lexington, Kentucky, USA date:6-10 July 2011
Abstract: Today, many different probabilistic programming languages exist and even more inference mechanisms for these languages. Still, most logic programming based languages use backward reasoning based on SLD resolution for inference. While these methods are typically computationally efficient, they often can neither handle infinite and/or continuous distributions, nor evidence. To overcome these limitations, we introduce distributional clauses, a variation and extension of Sato's distribution semantics. We also contribute a novel approximate inference method that integrates forward reasoning with importance sampling, a well-known technique for probabilistic inference. To achieve efficiency, we integrate two logic programming techniques to direct forward sampling. Magic sets are used to focus on relevant parts of the program, while the integration of backward reasoning allows one to identify and avoid regions of the sample space that are inconsistent with the evidence.
ISSN: 1471-0684
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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