The complexity of Bayesian networks specified by propositional and relational languages

Fabio G. Cozman, Denis D. Mauá

Resultado de la investigación: Contribución a una revistaArtículo

2 Citas (Scopus)

Resumen

© 2018 We examine the complexity of inference in Bayesian networks specified by logical languages. We consider representations that range from fragments of propositional logic to function-free first-order logic with equality; in doing so we cover a variety of plate models and of probabilistic relational models. We study the complexity of inferences when network, query and domain are the input (the inferential and the combined complexity), when the network is fixed and query and domain are the input (the query/data complexity), and when the network and query are fixed and the domain is the input (the domain complexity). We draw connections with probabilistic databases and liftability results, and obtain complexity classes that range from polynomial to exponential levels; we identify new languages with tractable inference, and we relate our results to languages based on plates and probabilistic relational models.
Idioma originalInglés estadounidense
Páginas (desde-hasta)96-141
Número de páginas46
PublicaciónArtificial Intelligence
DOI
EstadoPublicada - 1 sep 2018
Publicado de forma externa

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