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Title:First-order Probabilistic Inference Revisited
Author(s):Braz, Rodrigo de Salvo; Roth, Dan; Amir, Eyal
Subject(s):Artificial Intelligence
Abstract:Following ideas in Poole~\poole, which we correct, formalize and extend, this paper presents the first provable algorithm for reasoning with probabilistic first-order representations at the {\em lifted} level. Specifically, the algorithm automates the process of probabilistic reasoning about populations of individuals, their properties and the relations between them, without the need to ground the probabilistic knowledge base. The algorithm makes use of unification to guide an interleaving of variable ordering and first-order variable elimination. Importantly, our contribution includes the formalization of concepts necessary to reason about the algorithm's correctness and its correctness proof.
Issue Date:2004-06
Genre:Technical Report
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-14

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