Possibilistic networks parameter learning: Preliminary empirical comparison

Maroua Haddad, Philippe Leray, Amelie Levray, Karim Tabia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

ike Bayesian networks, possibilistic ones compactly encode joint uncertainty representations over a set of variables. Learning possibilistic networks from data in general and from imperfect or scarce data in particular, has not received enough attention. Indeed, only few works deal with learning the structure and the parameters of a possibilistic network from a dataset. This paper provides a preliminary comparative empirical evaluation of two approaches for learning the parameters of a possibilistic network from empirical data. The first method is a possibilistic approach while the second one first learns imprecise probability measures then transforms them into possibility distributions by means of probability-possibility transformations. The comparative evaluation focuses on learning belief networks on datasets with missing data and scarce datasets.
Original languageEnglish
Title of host publicationProceedings of the 8th Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes (JFRB’16), 2016
Place of PublicationClermont Ferrand, France
Number of pages13
Publication statusPublished - 2016
Event8th Francophone Days on Bayesian Networks and Probabilistic Graphical Models 2016 - Clermont Ferrand, France
Duration: 27 Jun 201628 Jun 2016

Conference

Conference8th Francophone Days on Bayesian Networks and Probabilistic Graphical Models 2016
Abbreviated titleJFRB'16
CountryFrance
CityClermont Ferrand
Period27/06/1628/06/16

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