Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Optimizing mpf queries: decision support and probabilistic inference
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
On probabilistic inference by weighted model counting
Artificial Intelligence
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Exploiting functional dependence in bayesian network inference
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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It is known that solving an exact inference problem on a discrete Bayesian network with many deterministic nodes can be far cheaper than what would be expected based on its treewidth. In this article, we introduce a novel technique for this: to the operations of factor multiplication and factor summation that form the basis of many inference algorithms, we add factor indexing. We integrate this operation into variable elimination, and extend the minweight heuristic accordingly. A preliminary empirical evaluation gives promising results.