Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Valuation-based systems for Bayesian decision analysis
Operations Research
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms
Statistics and Computing
Searching for the best elimination sequence in Bayesian networks by using ant colony optimization
Pattern Recognition Letters
Heuristic Algorithms for the Triangulation of Graphs
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Lazy propagation in junction trees
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Variable elimination (VE) and clustering algorithms (CAs) are two widely used algorithms for exact inference in Bayesian networks. Both the problem of finding an optimal variable elimination ordering in VE and the problem of finding an optimal graph triangulation in CAs are NP-complete, although greedy algorithms work well in practice. Usually, VE selects the next variable to be eliminated such that a new potential of minimum size is generated during the elimination process. CAs create a variable elimination sequence in order to triangulate the moral graph; usually, the next variable to be eliminated is selected such that a new clique of minimum size is created during the elimination process. This paper presents an approach which makes use of a criterion of minimum time (CMT) for the selection of the next variable to be eliminated in VE or in CAs, and compares its performance with that of the traditional approaches using a criterion of minimum size. The results show that, in general, the CMT introduced in this paper allows inference time to be reduced. Results regarding memory requirements are also reported.