Modeling climate variables using Bayesian finite mixture models

dc.contributor.advisorKeitt, Timothy H.en
dc.contributor.committeeMemberMüller, Peteren
dc.creatorCuthbertson, Thomas Edwinen
dc.date.accessioned2015-11-16T19:00:51Zen
dc.date.accessioned2018-01-22T22:29:09Z
dc.date.available2015-11-16T19:00:51Zen
dc.date.available2018-01-22T22:29:09Z
dc.date.issued2015-05en
dc.date.submittedMay 2015en
dc.date.updated2015-11-16T19:00:51Zen
dc.descriptiontexten
dc.description.abstractThis paper presents an alternative to point-based clustering models using a Bayesian finite mixture model. Using a simulation of soil moisture data in the Amazon region of South America, a Bayesian mixture of regressions is used to preserve periodic behavior within clusters. The mixture model provides a full probabilistic description of all uncertainties in the parameters that generated the data in addition to a clustering algorithm which better preserves the periodic nature of data at a particular pixel.en
dc.description.departmentStatisticsen
dc.format.mimetypeapplication/pdfen
dc.identifierdoi:10.15781/T2HD0Ven
dc.identifier.urihttp://hdl.handle.net/2152/32499en
dc.language.isoenen
dc.subjectBayesian finite mixture modelen
dc.subjectClimate simulationen
dc.subjectHierarchical modelsen
dc.subjectGrid approximationen
dc.subjectBayesen
dc.subjectBayesianen
dc.subjectGibbs samplingen
dc.titleModeling climate variables using Bayesian finite mixture modelsen
dc.typeThesisen

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