Please use this identifier to cite or link to this item: http://repositorio.ineel.mx/jspui/handle/123456789/311
23-Jun-2013
4. Versión publicada
Artículo
Titulo
A temporal bayesian network for diagnosis and prediction
Identificador del autor Tipo de identificacion Autor
CURPGUSTAVO ARROYO FIGUEROA
CURPLUIS ENRIQUE SUCAR SUCCAR
Descripcion/Resumen
Diagnosis and prediction m some domains, like medical and industrial diagnosis, require a representation that combines uncertainty management and temporal reasoning. Based on the fact that in many cases there are few state changes in the temporal range of interest, we propose a novel representation called Temporal Nodes Bayesian Network (TNBN). In a TNBN each node represents an event or state change of a variable, and an arc corresponds to a causal-temporal relation. The temporal intervals can differ in number and size for each temporal node, so this allows multiple granularity. Our approach is contrasted with a dynamic Bayesian network for a simple medical example. An empirical evaluation is presented for a more complex problem, a subsystem of a fossil power plant, in which this approach is used for fault diagnosis and event prediction with good results.
Area de conocimiento Campo Disciplina Subdisciplina
INGENIERÍA Y TECNOLOGÍACIENCIAS TECNOLÓGICASTECNOLOGÍA DE LOS ORDENADORESMODELOS CAUSALES
Audiencia
-
Materias
-
Acceso Abierto
Inglés

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