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dc.contributor.authorVanney, Peter
dc.date.accessioned2018-03-27T22:21:01Z
dc.date.available2018-03-27T22:21:01Z
dc.date.issued2016-05
dc.identifier.urihttp://hdl.handle.net/11122/8221
dc.descriptionMaster's Project (M.S.) University of Alaska Fairbanks, 2016en_US
dc.description.abstractIn this paper we apply hierarchical Bayesian predictive process models to historical precipitation data using the spBayes R package. Classical and hierarchical Bayesian techniques for spatial analysis and modeling require large matrix inversions and decompositions, which can take prohibitive amounts of time to run (n observations take time on the order of n3). Bayesian predictive process models have the same spatial framework as hierarchical Bayesian models but fit a subset of points (called knots) to the sample which allows for large scale dimension reduction and results in much smaller matrix inversions and faster computing times. These computationally less expensive models allow average desktop computers to analyze spatially related datasets in excess of 20,000 observations in an acceptable amount of time.en_US
dc.language.isoen_USen_US
dc.subjectPrecipitation (Meteorology)en_US
dc.subjectAlaskaen_US
dc.subjectHistoryen_US
dc.subjectStatisticsen_US
dc.subjectCanada, Westernen_US
dc.subjectBayesian statistical decision theoryen_US
dc.titleBayesian predictive process models for historical precipitation data of Alaska and southwestern Canadaen_US
dc.typeOtheren_US
dc.type.degreems
dc.identifier.departmentDepartment of Mathematics and Statistics
dc.contributor.chairShort, Margaret
dc.contributor.committeeGoddard, Scott
dc.contributor.committeeBarry, Ronald
refterms.dateFOA2020-03-05T15:09:20Z


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