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 proceedingConference contribution

8 Scopus citations

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 languageEnglish (US)
Title of host publicationFUSION 2016 - 19th International Conference on Information Fusion, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages798-804
Number of pages7
ISBN (Electronic)9780996452748
StatePublished - Aug 1 2016
Event19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany
Duration: Jul 5 2016Jul 8 2016

Other

Other19th International Conference on Information Fusion, FUSION 2016
CountryGermany
CityHeidelberg
Period7/5/167/8/16

ASJC Scopus subject areas

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

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