Computational-Workload Based Binarization and Partition of Qualitative Markov Trees for Belief Combination

  • Weiru Liu
  • Xin Hong
  • Kenny Adamson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2711)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Weiru Liu
    • 1
  • Xin Hong
    • 1
  • Kenny Adamson
    • 1
  1. 1.School of Computing and MathematicsUniversity of Ulster at JordanstownUK

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