The inferential complexity of bayesian and credal networks

Cassio Polpo de Campos, Fabio Gagliardi Cozman

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

55 Citations (Scopus)

Abstract

This paper presents new results on the complexity of graph-theoretical models that represent probabilities (Bayesian networks) and that represent interval and set valued probabilities (credal networks). We define a new class of networks with bounded width, and introduce a new decision problem for Bayesian networks, the maximin a posteriori. We present new links between the Bayesian and credal networks, and present new results both for Bayesian networks (most probable explanation with observations, maximin a posteriori) and for credal networks (bounds on probabilities a posteriori, most probable explanation with and without observations, maximum a posteriori).

Original languageEnglish
Title of host publicationInternational Joint Conference on Artificial Intelligence (IJCAI)
Pages1313-1318
Number of pages6
Publication statusPublished - 1 Dec 2005
Externally publishedYes
Event19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom
Duration: 30 Jul 20055 Aug 2005

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference19th International Joint Conference on Artificial Intelligence, IJCAI 2005
CountryUnited Kingdom
CityEdinburgh
Period30/07/055/08/05

Bibliographical note

(oral presentation, double-blind peer reviewed by >3 reviewers)

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