Belief in Belief Functions: An Examination of Shafer's Canonical Examples

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dc.contributor.authorLaskey, Kathryn B.
dc.date.accessioned2006-11-21T18:18:00Z
dc.date.available2006-11-21T18:18:00Z
dc.date.issued1989
dc.description.abstractIn the canonical examples underlying Shafer-Dempster theory, beliefs over the hypotheses of interest are derived from a probability model for a set of auxiliary hypotheses. A belief function differs from a Bayesian probability model in that one does not condition on those parts of the evidence for which no probabilities are specified. The significance of this difference in conditioning assumptions is illustrated with two examples giving rise to identical belief functions but different Bayesian probability distributions.
dc.description.sponsorshipWork supported in part by U. S. Army Communications Electronics Command, Contract No. DAAB07-86-C-A052.en
dc.format.extent444796 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.citationLaskey, Kathryn B. (1989). Belief in belief functions: An examination of Shafer's canonical examples. In Uncertainty in Artificial Intelligence 3, L.N. Kanal, T.S. Levitt, and J.F. Lemmer, eds., North-Holland.
dc.identifier.isbn0-444-88650-8
dc.identifier.urihttps://hdl.handle.net/1920/1738
dc.language.isoen
dc.publisherNorth-Hollanden
dc.relation.ispartofseriesC4I-89-01en
dc.titleBelief in Belief Functions: An Examination of Shafer's Canonical Examples
dc.typeBook chapter

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