Propositional and relational Bayesian networks associated with imprecise and qualitative probabilistic assessments

Authors:
Fabio Gagliardi Cozman;Cassio Polpo de Campos;Jaime Shinsuke Ide;José Carlos Ferreira da Rocha
Affiliations:
Universidade de São Paulo, São Paulo, SP - Brazil;Pontificia Universidade Católica, São Paulo, SP - Brazil;Universidade de São Paulo, São Paulo, SP - Brazil;Universidade Estadual de Ponta Grossa, Ponta Grossa, PR - Brazil
Venue:
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Year:
2004

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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.