2017/07/12 by Mieczysław A. Kłopotek, Kłopotek, Mieczysław A.
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1707.03872
1994 internal report
arxiv created 2017/07/12 · arxiv updated 2017/07/14
Several approaches of structuring (factorization, decomposition) of Dempster-Shafer joint belief functions from literature are reviewed with special emphasis on their capability to capture independence from the point of view of the claim that belief functions generalize bayes notion of probability. It is demonstrated that Zhu and Lee's Zhu:93 logical networks and Smets' Smets:93 directed acyclic graphs are unable to capture statistical dependence/independence of bayesian networks Pearl:88. On the other hand, though Shenoy and Shafer's hypergraphs can explicitly represent bayesian network factorization of bayesian belief functions, they disclaim any need for representation of independence of variables in belief functions. Cano et al. Cano:93 reject the hypergraph representation of Shenoy and Shafer just on grounds of missing representation of variable independence, but in their frameworks some belief functions factorizable in Shenoy/Shafer framework cannot be factored. The approach in Klopotek:93f on the other hand combines the merits of both Cano et al. and of Shenoy/Shafer approach in that for Shenoy/Shafer approach no simpler factorization than that in Klopotek:93f approach exists and on the other hand all independences among variables captured in Cano et al. framework and many more are captured in Klopotek:93f approach.%