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R174
BEEM : Bucket Elimination with External Memory

Kalev Kask, Rina Dechter and Andrew E. Gelfand

Abstract
A major limitation of exact inference algorithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend inference algorithms, particularly Bucket Elim- ination, a special case of cluster (join) tree decomposition, to utilize disk memory. We pro- vide the underlying ideas and show promising empirical results of exactly solving large problems not solvable before.

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