A new uncertainty measure for belief networks with applications to optimal evidential inferencing

Jiming LIU*, David A. Maluf, Michel C. Desmarais

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

This paper is concerned with the problem of measuring the uncertainty in a broad class of belief networks, as encountered in evidential reasoning applications. In our discussion, we give an explicit account of the networks concerned, and coin them the Dempster-Shafer (D-S) belief networks. We examine the essence and the requirement of such an uncertainty measure based on well-defined discrete event dynamical systems concepts. Furthermore, we extend the notion of entropy for the D-S belief networks in order to obtain an improved optimal dynamical observer. The significance and generality of the proposed dynamical observer of measuring uncertainty for the D-S belief networks lie in that it can serve as a performance estimator as well as a feedback for improving both the efficiency and the quality of the D-S belief network-based evidential inferencing. We demonstrate, with Monte Carlo simulation, the implementation and the effectiveness of the proposed dynamical observer in solving the problem of evidential inferencing with optimal evidence node selection.

Original languageEnglish
Pages (from-to)416-425
Number of pages10
JournalIEEE Transactions on Knowledge and Data Engineering
Volume13
Issue number3
DOIs
Publication statusPublished - May 2001

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • Belief networks
  • Controller
  • Discrete event dynamical systems
  • Entropy
  • Observer
  • Optimal evidential inferencing
  • Uncertainty modeling and management
  • User profile assessment

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