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. In this paper, we present an alternative algorithm of transforming 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. These three types of binary trees are examined to reveal the structural and computational differences.
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Liu, W., Hong, X., Adamson, K. (2003). Computational-Workload Based Binarization and Partition of Qualitative Markov Trees for Belief Combination. In: Nielsen, T.D., Zhang, N.L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2003. Lecture Notes in Computer Science(), vol 2711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45062-7_25
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DOI: https://doi.org/10.1007/978-3-540-45062-7_25
Publisher Name: Springer, Berlin, Heidelberg
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