By Topic

Shafer-Dempster and Bayesian reasoning: a response to `Shafer-Dempster reasoning with applications to multisensor target identification systems'

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
D. M. Buede ; Decision Logistics, Reston, VA, USA

In a previously published paper (ibid., vol.17, no.6, p.968-77, 1987) P.L. Bogler made four points that require further clarification and/or correction concerning Bayesian probabilistic reasoning for the multisensor fusion of identification data: 1) the Bayesian approach forces a common level of abstraction to be defined for all sensors, a level of abstraction that is not meaningful for some sensors; 2) Bayesian results can be unstable and intuitively unsatisfying; 3) Bayesian results are not commutative; and 4) Bayesian results for friend/foe identification can force false inferences concerning the identification of specific aircraft types. These assertions are reviewed and shown to be incorrect. In addition, it is shown that all of the examples of Dempster's rule of combination are identical to Bayesian probability theory. The contention here is not that the Shafer-Dempster approach to uncertainty management is identical to Bayesian probability theory, but rather that the Shafer-Dempster approach is not fully illustrated

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:18 ,  Issue: 6 )