Generalised Extreme Value geoadditive model analysis via variational Bayes

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dc.contributor.author Nevillea, Sarah en_US
dc.contributor.author Wand, Matt en_US
dc.contributor.editor Alfred Stein, Edzer Pebesma and Gerard Heuvelink en_US
dc.date.accessioned 2012-10-12T03:35:47Z
dc.date.available 2012-10-12T03:35:47Z
dc.date.issued 2011 en_US
dc.identifier 2011004583 en_US
dc.identifier.citation Nevillea Sarah and Wand Matthew 2011, 'Generalised Extreme Value geoadditive model analysis via variational Bayes', , Elsevier, The Netherlands, , pp. 8-13. en_US
dc.identifier.issn 1878-0296 en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/19063
dc.description.abstract We devise a variationalBayes algorithm for fast approximate inference in Bayesian GeneralizedExtremeValue additive modelanalysis. Such models are useful for flexibly assessing the impact of continuous predictor variables on sample extremes. The new methodology allows large Bayesian models to be fitted and assessed without the significant computing costs of Monte Carlo methods en_US
dc.language en_US
dc.publisher Elsevier en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.proenv.2011.02.003 en_US
dc.title Generalised Extreme Value geoadditive model analysis via variational Bayes en_US
dc.parent Procedia Environmental Sciences en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation The Netherlands en_US
dc.identifier.startpage 8 en_US
dc.identifier.endpage 13 en_US
dc.cauo.name SCI.Faculty of Science en_US
dc.conference Verified OK en_US
dc.for 010400 en_US
dc.personcode 0000081456 en_US
dc.personcode 110509 en_US
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom 1st Conference on Spatial Statistics 2011 ? Mapping Global Change en_US
dc.date.activity 20110308 en_US
dc.location.activity Enschede en_US
dc.description.keywords Auxiliary mixture sampling; Bayesian inference; generalized additive models; sample extremes; variational approximation en_US
dc.staffid 110509 en_US


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