Please use this identifier to cite or link to this item: http://repositorio.ineel.mx/jspui/handle/123456789/311
A temporal bayesian network for diagnosis and prediction
GUSTAVO ARROYO FIGUEROA
LUIS ENRIQUE SUCAR SUCCAR
Acceso Abierto
Artículo
Inglés
23-Jun-2013
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.
http://repositorio.ineel.mx/jspui/handle/123456789/311
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