DL-Lite Bayesian networks: A tractable probabilistic graphical model

Denis D. Mauá, Fabio G. Cozman

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferencia

2 Citas (Scopus)

Resumen

© Springer International Publishing Switzerland 2015. The construction of probabilistic models that can represent large systems requires the ability to describe repetitive and hierarchical structures. To do so, one can resort to constructs from description logics. In this paper we present a class of relational Bayesian networks based on the popular description logic DL-Lite. Our main result is that, for this modeling language, marginal inference and most probable explanation require polynomial effort. We show this by reductions to edge covering problems, and derive a result of independent interest; namely, that counting edge covers in a particular class of graphs requires polynomial effort.
Idioma originalInglés estadounidense
Título de la publicación alojadaDL-Lite Bayesian networks: A tractable probabilistic graphical model
Páginas50-64
Número de páginas15
ISBN (versión digital)9783319235394
DOI
EstadoPublicada - 1 ene 2015
Publicado de forma externa
EventoLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duración: 1 ene 2018 → …

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen9310
ISSN (versión impresa)0302-9743

Conferencia

ConferenciaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Período1/01/18 → …

Huella Profundice en los temas de investigación de 'DL-Lite Bayesian networks: A tractable probabilistic graphical model'. En conjunto forman una huella única.

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    Mauá, D. D., & Cozman, F. G. (2015). DL-Lite Bayesian networks: A tractable probabilistic graphical model. En DL-Lite Bayesian networks: A tractable probabilistic graphical model (pp. 50-64). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9310). https://doi.org/10.1007/978-3-319-23540-0_4