A generalization of Bayesian inference in the Dempster-Shafer belief theoretic framework

J. N. Heendeni; Kamal Premaratne; Manohar Murthi; Joseph Uscinski; M. Scheutz

A generalization of Bayesian inference in the Dempster-Shafer belief theoretic framework

J. N. Heendeni, Kamal Premaratne, Manohar Murthi, Joseph Uscinski, M. Scheutz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In the literature, two main views of Dempster-Shafer (DS) theory are espoused: DS theory as evidence (as described in Shafer's seminal book) and DS theory as a generalization of probability. These two views are not always consistent. In this paper, we employ the generalized probability view of DS theory to arrive at results that allow one to perform Bayesian inference within the DS theoretic (DST) framework. The importance of this generalization is its capability of handling a wider variety of data imperfections, a feature inherited from the DST framework. In the process of developing these results akin to Bayesian inference, we also arrive at an evidence combination strategy which is consistent with the generalized probability view of DS theory, a feature lacking in the popular Dempster's combination rule (DCR). Finally, using the data from a political science survey, we demonstrate the application of our results on an experiment which attempts to gauge the hidden attitude of an individual from his/her observed behavior.

Original language	English (US)
Title of host publication	FUSION 2016 - 19th International Conference on Information Fusion, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	798-804
Number of pages	7
ISBN (Electronic)	9780996452748
State	Published - Aug 1 2016
Event	19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany Duration: Jul 5 2016 → Jul 8 2016

Other

Other	19th International Conference on Information Fusion, FUSION 2016
Country	Germany
City	Heidelberg
Period	7/5/16 → 7/8/16

ASJC Scopus subject areas

Statistics, Probability and Uncertainty
Computer Science Applications
Computer Vision and Pattern Recognition
Signal Processing

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A generalization of Bayesian inference in the Dempster-Shafer belief theoretic framework. / Heendeni, J. N.; Premaratne, Kamal ; Murthi, Manohar ; Uscinski, Joseph; Scheutz, M.

FUSION 2016 - 19th International Conference on Information Fusion, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 798-804 7527968.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Heendeni, JN, Premaratne, K , Murthi, M , Uscinski, J & Scheutz, M 2016, A generalization of Bayesian inference in the Dempster-Shafer belief theoretic framework. in FUSION 2016 - 19th International Conference on Information Fusion, Proceedings., 7527968, Institute of Electrical and Electronics Engineers Inc., pp. 798-804, 19th International Conference on Information Fusion, FUSION 2016, Heidelberg, Germany, 7/5/16.

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