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Author Taghipour, Nima ♦ Fierens, Daan ♦ Van, Guy ♦ Jesse, Broeck ♦ Blockeel, Davis Hendrik
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Description Lifting aims at improving the efficiency of probabilistic inference by exploiting symme-tries in the model. Various methods for lifted probabilistic inference have been proposed, but our understanding of these methods and the relationships between them is still lim-ited, compared to their propositional coun-terparts. The only existing theoretical char-acterization of lifting is a completeness re-sult for weighted first-order model counting. This paper addresses the question whether the same completeness result holds for other lifted inference algorithms. We answer this question positively for lifted variable elimina-tion (LVE). Our proof relies on introducing a novel inference operator for LVE. 1
In Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2013-01-01