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http://hdl.handle.net/20.500.11765/3809
Bayesian networks for probabilistic weather prediction
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Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Cofiño González, Antonio Santiago | es_ES |
dc.contributor.author | Cano Trueba, Rafael | es_ES |
dc.contributor.author | Sordo, Carmen María | es_ES |
dc.contributor.author | Gutiérrez Llorente, José Manuel | es_ES |
dc.date.accessioned | 2016-08-05T10:57:38Z | - |
dc.date.available | 2016-08-05T10:57:38Z | - |
dc.date.issued | 2002 | - |
dc.identifier.citation | XV European Conference on Artificial Intelligence (2002) | es_ES |
dc.identifier.uri | http://hdl.handle.net/20.500.11765/3809 | - |
dc.description | Ponencia presentada en: 15th European Conference on Artificial Intelligence celebrada los días 21-26 de julio en Lyon, Francia | es_ES |
dc.description.abstract | Several 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.iso | eng | es_ES |
dc.rights | Licencia CC: Reconocimiento–NoComercial–SinObraDerivada CC BY-NC-ND | es_ES |
dc.subject | Time series prediction | es_ES |
dc.subject | Bayesian Networks | es_ES |
dc.subject | Acyclic graph | es_ES |
dc.subject | Rainfall forecast | es_ES |
dc.subject | Probabilistic weather prediction | es_ES |
dc.title | Bayesian networks for probabilistic weather prediction | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
Colecciones: | Otras actas-congresos |
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