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Proceedings Paper

Comparison of Bayesian and Dempster-Shafer theory for sensing: a practitioner's approach
Author(s): James C. Hoffman; Robin R. Murphy
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Paper Abstract

This paper presents an applied practical comparison of Bayesian and Dempster-Shafer techniques useful for managing uncertainty in sensing. Three formulations of the same example are presented: a Bayesian, a naive Dempster-Shafer, and a Dempster-Shafer approach using a refined frame of discernment. Both the Bayesian and Dempster-Shafer (with a refined frame of discernment) yield similar results; however, information content and representations are different between the two methods. Bayesian theory requires a more explicit formulation of conditioning and the prior probabilities of events. Dempster-Shafer theory embeds conditioning information into its belief function and does not rely on prior knowledge, making it appropriate for situations where it is difficult to either collect or posit such probabilities, or isolate their contribution.

Paper Details

Date Published: 29 October 1993
PDF: 14 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162045
Show Author Affiliations
James C. Hoffman, Colorado School of Mines (United States)
Robin R. Murphy, Colorado School of Mines (United States)

Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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