Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Conditioning graphs: practical structures for inference in bayesian networks
Conditioning graphs: practical structures for inference in bayesian networks
Parallel tree contraction and its application
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Query DAGs: a practical paradigm for implementing belief-network inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Conditioning graphs: practical structures for inference in bayesian networks
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Exploiting dynamic independence in a static conditioning graph
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
On the structure of elimination trees for Bayesian network inference
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
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An elimination tree is a form of recursive factorization for Bayesian networks. Elimination trees can be used as the basis for a practical implementation of Bayesian network inference via conditioning graphs. The time complexity for inference in elimination trees has been shown to be O(nexp(d)), where d is the height of the elimination tree. In this paper, we demonstrate two new heuristics for building small elimination trees. We also demonstrate a simple technique for deriving elimination trees from Darwiche et al.'s dtrees, and vice versa. We show empirically that our heuristics, combined with a constructive process for building elimination trees, produces the smaller elimination trees than previous methods.