An approximation of surprise index as a measure of confidence

Zagorecki, A and Kozniewski, M and Druzdzel, MJ (2015) An approximation of surprise index as a measure of confidence. In: UNSPECIFIED.

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Abstract

Probabilistic graphical models, such as Bayesian networks, are intuitive and theoretically sound tools for modeling uncertainty. A major problem with applying Bayesian networks in practice is that it is hard to judge whether a model fits well a case that it is supposed to solve. One way of expressing a possible dissonance between a model and a case is the surprise index, proposed by Habbema, which expresses the degree of surprise by the evidence given the model. While this measure reflects the intuition that the probability of a case should be judged in the context of a model, it is computationally intractable. In this paper, we propose an efficient way of approximating the surprise index.

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Details

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Status:	Published
Creators/Authors:	Creators Email Pitt Username ORCID Zagorecki, A Kozniewski, M Druzdzel, MJ marek@sis.pitt.edu DRUZDZEL 0000-0002-7598-2286
Date:	1 January 2015
Date Type:	Publication
Journal or Publication Title:	AAAI Fall Symposium - Technical Report
Volume:	FS-15-
Page Range:	39 - 41
Event Type:	Conference
Schools and Programs:	School of Information Sciences > Information Science
Refereed:	Yes
ISBN:	9781577357513
Date Deposited:	19 Jul 2016 12:59
Last Modified:	30 Mar 2021 13:55
URI:	http://d-scholarship.pitt.edu/id/eprint/28603

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