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Bayesian predictive process models for historical precipitation data of Alaska and southwestern Canada

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dc.contributor.author Vanney, Peter
dc.date.accessioned 2018-03-27T22:21:01Z
dc.date.available 2018-03-27T22:21:01Z
dc.date.issued 2016-05
dc.identifier.uri http://hdl.handle.net/11122/8221
dc.description Master's Project (M.S.) University of Alaska Fairbanks, 2016 en_US
dc.description.abstract In 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.iso en_US en_US
dc.subject Precipitation (Meteorology) en_US
dc.subject Alaska en_US
dc.subject History en_US
dc.subject Statistics en_US
dc.subject Canada, Western en_US
dc.subject Bayesian statistical decision theory en_US
dc.title Bayesian predictive process models for historical precipitation data of Alaska and southwestern Canada en_US
dc.type Other en_US
dc.type.degree ms
dc.identifier.department Department of Mathematics and Statistics
dc.contributor.chair Short, Margaret
dc.contributor.committee Goddard, Scott
dc.contributor.committee Barry, Ronald


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