Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments

F. G. Cozman, C. P. de Campos, J. S. Ide, J. C. F. da Rocha

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

Abstract

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.
Original languageEnglish
Title of host publicationConference on Uncertainty in Artificial Intelligence (UAI)
Place of PublicationBanff
PublisherAUAI Press
Pages104-111
Number of pages8
Publication statusPublished - 2004

Bibliographical note

(top 10%, plenary presentation, double-blind peer reviewed by >3 reviewers)

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    Cozman, F. G., de Campos, C. P., Ide, J. S., & da Rocha, J. C. F. (2004). Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments. In Conference on Uncertainty in Artificial Intelligence (UAI) (pp. 104-111). AUAI Press. http://www.eeecs.qub.ac.uk/~c.decampos/publist/papers/decampos2004c.pdf