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dc.contributor.authorLiu, Liping-
dc.contributor.authorShenoy, Catherine-
dc.contributor.authorShenoy, Prakash P.-
dc.date.accessioned2004-12-14T22:09:38Z-
dc.date.available2004-12-14T22:09:38Z-
dc.date.issued2003-08-
dc.identifier.citationU. Kjærulff and C. Meek (eds.), Uncertainty in Artificial Intelligence, 2003, pp. 370--377, Morgan Kaufmann, San Francisco, CAen
dc.identifier.isbn0-127-05664-5-
dc.identifier.urihttp://hdl.handle.net/1808/154-
dc.descriptionThis paper is a condensed 8-pp version of a longer paper titled "Knowledge Representation and Integration for Portfolio Evaluation Using Linear Belief Functions," School of Business Working Paper, December 2003, that has been conditionally accepted in Nov. 2004 for publication in IEEE Transactions on Systems, Man & Cybernetics, Part A.en
dc.description.abstractWe show how to use linear belief functions to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, dis-tributional assumptions, linear relations, and em-pirical asset pricing models. We then appeal to Dempster’s rule of combination to integrate the knowledge for assessing an overall belief on portfolio performance, and to update this belief by incorporating additional information.en
dc.format.extent1727612 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen_US-
dc.publisherMorgan Kaufmann Publishersen
dc.subjectNormal belief functionsen
dc.subjectMultivariate normal distributionen
dc.subjectDempster-Shafer belief function theoryen
dc.subjectPortfolio theoryen
dc.titleA Linear Belief Function Approach to Portfolio Evaluationen
dc.typeBook chapteren
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