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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

© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. 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:
CreatorsEmailPitt UsernameORCID
Zagorecki, A
Kozniewski, M
Druzdzel, MJdruzdzel@pitt.eduDRUZDZEL
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: 08 Dec 2017 13:55
URI: http://d-scholarship.pitt.edu/id/eprint/28603

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