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A comparison of structural distance measures for causal Bayesian network models

de Jongh, Martijn and Druzdzel, Marek J. (2009) A comparison of structural distance measures for causal Bayesian network models. In: Recent Advances in Intelligent Information Systems, Challenging Problems of Science, Computer Science series. Academic Publishing House EXIT, Warsaw, Poland, pp. 443-456. ISBN 9788360434598

Abstract

We compare measures of structural distance between both, Bayesian networks Aand equivalence classes of Bayesian networks. The main application of these measures is in learning algorithms, where typically the interest is in how accurately a gold standard structure is retrieved by a learning algorithm. Structural distance measures can be especially useful when looking for causal structures. We discuss desirable properties of measures, review existing measures, and show some of our empirical findings concerning the performance of these metrics in practice.


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Details

Item Type: Book Section
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
de Jongh, Martijnm.a.dejongh@gmail.com
Druzdzel, Marek J.marek@sis.pitt.eduDRUZDZEL
Date: 2009
Date Type: Publication
Publisher: Academic Publishing House EXIT
Place of Publication: Warsaw, Poland
Page Range: pp. 443-456
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9788360434598
Title of Book: Recent Advances in Intelligent Information Systems, Challenging Problems of Science, Computer Science series
Editors:
EditorsEmailPitt UsernameORCID
Klopotek, MieczyslawUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Przepiorkowski, AdamUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wierzchon, Slawomir T.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Trojanowski, KrzysztofUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date Deposited: 12 Jul 2011 14:08
Last Modified: 15 Nov 2016 13:35
URI: http://d-scholarship.pitt.edu/id/eprint/6011

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