A parallel approach to evidence combination on qualitative Markov trees

Abstract

Dempster's rule of evidence combination is computational expensive. We present 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.