{"type":"ScholarlyArticle","title":"Probabilistic Partial Evaluation: Exploiting Rule Structure in Probabilistic Inference","authors":[{"name":"David Poole","slug":"david-poole","given":"David","family":"Poole","affiliation":"Department of Computer Science University of British Columbia 2366 Main Mall , Vancouver , B.C. , Canada V6T 1Z4"}],"abstract":"Bayesian belief networks have grown to prominence because they provide compact representations of many domains, and there are algorithms to exploit this compactness. The next step is to allow compact representations of the conditional probability tables of a variable given its parents. In this paper we present such a representation in terms of parent contexts and provide an algorithm that exploits this compactness. The representation is in terms of rules that provide conditional probabilities in different contexts. The algorithm is based on eliminating the variables not needed in an answer in turn. The operations for eliminating a variable correspond to a form of partial evaluation, where we are careful to maintain the probabilistic dependencies necessary for correct probabilistic inference. We show how this new method can exploit more structure than previous methods for structured belief network inference.","doi":null,"url":"https://ijcai.org/proceedings/1997/185","pdf":"https://ijcai.org/proceedings/1997/0185.pdf","markdown":"https://ijcai.org/proceedings/1997/185.md","bibtex":"https://ijcai.org/proceedings/1997/bibtex/185","volume":{"slug":"1997","title":"Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1997","url":"https://ijcai.org/proceedings/1997/","year":1997,"location":"Nagoya, Japan","dates":"","editors":"Martha E. Pollack","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Probabilistic Reasoning","track":"Probabilistic Reasoning","number":185,"pages":{"first":1284,"last":1290},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Becker and Geiger, 1996] A. Becker and D. Geiger. A suffi- ciently fast algorithm for finding close to optimal junction trees. In E. Horvitz and F. Jensen, editor, Proc. Twelfth Conf on Uncertainty in Artificial Intelligence (UA/-96), pages 81-89, Portland, Oregon, 1996.","title":"A sufficiently fast algorithm for finding close to optimal junction trees","authors":"A Becker, D Geiger","year":"1996","venue":"Proc. Twelfth Conf on Uncertainty in Artificial Intelligence (UA/-96)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Boutilier et al., 1995] C. Boutilier, R. Dearden, and M. Goldszmidt. Exploiting structure in policy construc- tion. 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