AAAI Publications, Twenty-Fourth AAAI Conference on Artificial Intelligence

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New Mini-Bucket Partitioning Heuristics for Bounding the Probability of Evidence
Emma Rollon, Rina Dechter

Last modified: 2010-07-04


Mini-Bucket Elimination (MBE) is a well-known approximation algorithm deriving lower and upper bounds on quantities of interest over graphical models. It relies on a procedure that partitions a set of functions, called bucket, into smaller subsets, called mini-buckets. The method has been used with a single partitioning heuristic throughout, so the impact of the partitioning algorithm on the quality of the generated bound has never been investigated. This paper addresses this issue by presenting a framework within which partitioning strategies can be described, analyzed and compared. We derive a new class of partitioning heuristics from first-principles geared for likelihood queries, demonstrate their impact on a number of benchmarks for probabilistic reasoning and show that the results are competitive (often superior) to state-of-the-art bounding schemes.


Probabilistic Inference; Bayesian Networks; Graphical Models

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