Approximating MAP Inference in Credal Networks Using Probability-Possibility Transformations

Salem Benferhat, Amelie Levray, Karim Tabia

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

Abstract

This paper focuses on belief graphical models and provides an efficient approximation of MAP inference in credal networks using probability-possibility transformations. We first present two transformations from credal networks to possibilistic ones that are suitable for MAP inference in credal networks. Then we present four criteria to evaluate our approximate MAP inference. The last part of the paper provides experimental studies that compare our approach with both standard exact and approximate MAP inference in credal networks. The paper also provides a brief analysis of MAP inference complexity using possibilistic networks and the results definitely open new perspectives for MAP inference in credal networks.
Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Tools with Artificial Intelligence (ICTAI’17), 2017
Place of PublicationBoston, MA, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1057-1064
Number of pages8
ISBN (Electronic)978-1-5386-3876-7
ISBN (Print)978-1-5386-3877-4
DOIs
Publication statusPublished - 7 Jun 2018
Event29th IEEE International Conference on Tools with Artificial Intelligence - Boston, United States
Duration: 6 Nov 20178 Nov 2017
http://ictai2017.org

Publication series

Name
PublisherIEEE
ISSN (Electronic)2375-0197

Conference

Conference29th IEEE International Conference on Tools with Artificial Intelligence
Abbreviated titleIEEE ICTAI 2017
Country/TerritoryUnited States
CityBoston
Period6/11/178/11/17
Internet address

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