Abstract
The paper focuses on the task of generating the first m best solutions for a combinatorial optimization problem defined over a graphical model (e.g., the m most probable explanations for a Bayesian network). We show that the m-best task can be expressed within the unifying framework of semirings making known inference algorithms defined and their correctness and completeness for the m-best task immediately implied. We subsequently describe elim-m-opt, a new bucket elimination algorithm for solving the m-best task, provide algorithms for its defining combination and marginalization operators and analyze its worst-case performance. An extension of the algorithm to the mini-bucket framework provides bounds for each of the m best solutions. Empirical demonstrations of the algorithms with emphasis on their potential for approximations are provided.
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Flerova, N., Rollon, E., Dechter, R. (2012). Bucket and Mini-bucket Schemes for M Best Solutions over Graphical Models. In: Croitoru, M., Rudolph, S., Wilson, N., Howse, J., Corby, O. (eds) Graph Structures for Knowledge Representation and Reasoning. Lecture Notes in Computer Science(), vol 7205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29449-5_4
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DOI: https://doi.org/10.1007/978-3-642-29449-5_4
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