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Bayesian networks for probabilistic weather prediction
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dc.contributor.authorCofiño, Antonio S.es_ES
dc.contributor.authorCano Trueba, Rafaeles_ES
dc.contributor.authorSordo, Carmen Maríaes_ES
dc.contributor.authorGutiérrez Llorente, José Manueles_ES
dc.date.accessioned2016-08-05T10:57:38Z-
dc.date.available2016-08-05T10:57:38Z-
dc.date.issued2002-
dc.identifier.citationXV European Conference on Artificial Intelligence (2002)es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.11765/3809-
dc.descriptionPonencia presentada en: 15th European Conference on Artificial Intelligence celebrada los días 21-26 de julio en Lyon, Franciaes_ES
dc.description.abstractSeveral standard approaches have been introduced for meteorological time series prediction (analog techniques, neural networks, etc.). However, when dealing with multivariate spatially distributed time series (e.g., a network of meteorological stations over the Iberian peninsula) the above methods do not consider all the available information (they consider special independency assumptions to simplify the model). In this work, we introduce Bayesian Networks (BNs) in this framework to model the spatial and temporal dependencies among the different stations using a directed acyclic graph.es_ES
dc.language.isoenges_ES
dc.rightsLicencia CC: Reconocimiento–NoComercial–SinObraDerivada CC BY-NC-NDes_ES
dc.subjectTime series predictiones_ES
dc.subjectBayesian Networkses_ES
dc.subjectAcyclic graphes_ES
dc.subjectRainfall forecastes_ES
dc.subjectProbabilistic weather predictiones_ES
dc.titleBayesian networks for probabilistic weather predictiones_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
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