Uncertainty in Artificial Intelligence
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Bucket Elimination: A Unifying Framework for Several Probabilistic Inference
Rina Dechter
Abstract:
Probabilistic inference algorithms for finding the most probable explanation, the maximum aposteriori hypothesis, and the maximum expected utility and for updating belief are reformulated as an elimination--type algorithm called bucket elimination. This emphasizes the principle common to many of the algorithms appearing in that literature and clarifies their relationship to nonserial dynamic programming algorithms. We also present a general way of combining conditioning and elimination within this framework. Bounds on complexity are given for all the algorithms as a function of the problem's structure.
Keywords:
Pages: 211-219
PS Link: FTP://ftp.ics.uci.edu/pub/CSP-repository/papers/bucket-elimination.ps
PDF Link: /papers/96/p211-dechter.pdf
BibTex:
@INPROCEEDINGS{Dechter96,
AUTHOR = "Rina Dechter ",
TITLE = "Bucket Elimination: A Unifying Framework for Several Probabilistic Inference",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "211--219"
}


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