A Parallel Approach to Evidence Combination on Qualitative Markov trees
نویسندگان
چکیده
Dempster's rule of evidence combination is computational expensive. This paper presents a parallel approach to evidence combination on a qualitative Markov tree. Binarization algorithm transforms a qualitative Markov tree into a binary tree based on the computational workload in nodes for an exact implementation of evidence combination. A binary tree is then partitioned into clusters with each cluster being assigned to a processor in a parallel environment. The parallel implementation improves the computational efficiency of evidence combination.
منابع مشابه
Computational-Workload Based Binarization and Partition of Qualitative Markov Trees for Belief Combination
Weiru Liu, Xin Hong, and Kenny Adamson School of Computing and Mathematics, University of Ulster at Jordanstown, UK [email protected] Abstract. Binary join trees have been a popular structure to compute the impact of multiple belief functions initially assigned to nodes of trees or networks. Shenoy has proposed two alternative methods to transform a qualitative Markov tree into a binary tree. ...
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