The Inferential Complexity of Bayesian and Credal Networks

C. P. de Campos, F. G. Cozman

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

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 - 2005

Bibliographical note

(top 18%, oral presentation, double-blind peer reviewed by >3 reviewers)

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