Inference with aggregation parfactors: Lifted elimination with first-order d-separation

Felipe Iwao Takiyama, Fabio Gagliardi Cozman

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

1 Cita (Scopus)

Resumen

© 2014 IEEE. In this paper we focus on lifted inference for statistical relational models, that is, inference that avoids complete grounding, in models that combine logical and probabilistic assertions. We focus on relational Bayesian networks that can be represented through par factors and aggregation par factors. We present a new elimination rule for lifted variable elimination, and show how to use first-order d-separation to extend the reach of existing elimination rules.
Idioma originalInglés estadounidense
Páginas384-389
Número de páginas6
DOI
EstadoPublicada - 1 ene 2014
Publicado de forma externa
EventoProceedings - 2014 Brazilian Conference on Intelligent Systems, BRACIS 2014 -
Duración: 1 ene 2014 → …

Conferencia

ConferenciaProceedings - 2014 Brazilian Conference on Intelligent Systems, BRACIS 2014
Período1/01/14 → …

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    Takiyama, F. I., & Cozman, F. G. (2014). Inference with aggregation parfactors: Lifted elimination with first-order d-separation. 384-389. Papel presentado en Proceedings - 2014 Brazilian Conference on Intelligent Systems, BRACIS 2014, . https://doi.org/10.1109/BRACIS.2014.75