Belief updating and learning in semi-qualitative probabilistic networks

Cassio Polpo De Campos, Fabio Gagliardi Cozman

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

21 Citas (Scopus)

Resumen

This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively through multilinear programming. We then discuss learning: we consider a maximum likelihood method that generates point estimates given a SQPN and empirical data, and we describe a Bayesian-minded method that employs the Imprecise Dirichlet Model to generate set-valued estimates.
Idioma originalInglés estadounidense
Páginas153-160
Número de páginas8
EstadoPublicada - 1 dic 2005
Publicado de forma externa
EventoProceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005 -
Duración: 1 dic 2005 → …

Conferencia

ConferenciaProceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005
Período1/12/05 → …

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  • Citar esto

    De Campos, C. P., & Cozman, F. G. (2005). Belief updating and learning in semi-qualitative probabilistic networks. 153-160. Papel presentado en Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, .