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

We are 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 call them the Dempster-Shafer (D-S) belief networks. We examine the e...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2001-05, Vol.13 (3), p.416-425
Main Authors: Jiming Liu, Maluf, D.A., Desmarais, M.C.
Format: Article
Language:English
Subjects:
Application software
Bayesian methods
Belief networks
Computer network management
Computer networks
Constraint theory
Entropy
Feedback
Knowledge representation
Measurement uncertainty
Optimal control
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We are 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 call 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.
ISSN:1041-4347
1558-2191
DOI:10.1109/69.929899