dc.contributor.advisor | Eidsvik, Jo | nb_NO |
dc.contributor.author | Dahl, Morten | nb_NO |
dc.date.accessioned | 2014-12-19T13:58:49Z | |
dc.date.available | 2014-12-19T13:58:49Z | |
dc.date.created | 2011-06-27 | nb_NO |
dc.date.issued | 2007 | nb_NO |
dc.identifier | 426877 | nb_NO |
dc.identifier | ntnudaim:1513 | nb_NO |
dc.identifier.uri | http://hdl.handle.net/11250/258822 | |
dc.description.abstract | In this paper we look at generalised linear spatial models, in a bayesian setting. For inference we use a special approxomative technique known as the Laplace approximation. We examine a dataset consisting of radionucleide counts sampled over Rongelap Island.The marginal likelihood of a statistical model, can be used as a measure of model quality. Using the Laplace transformation, combined with a special integration technique, we calculate the marginal likelihood for different spatial models. We also discuss spatial designs. We create a set of retrospective designs based on intuition which we compare with a design created by a sequential removal procedure. We use the so called measure of information to compare designs. We calculate the measure of design both based on the real data, and a simulated dataset, which was created using information inferred from the real dataset. We discuss aspects of the individual designs, and how to best calculate the measure of information. | nb_NO |
dc.language | eng | nb_NO |
dc.publisher | Institutt for matematiske fag | nb_NO |
dc.subject | ntnudaim:1513 | no_NO |
dc.subject | SIF3 fysikk og matematikk | no_NO |
dc.subject | Industriell matematikk | no_NO |
dc.title | Model Choice and Experimental Design for Generalized Linear Spatial Models | nb_NO |
dc.type | Master thesis | nb_NO |
dc.source.pagenumber | 50 | nb_NO |
dc.contributor.department | Norges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for matematiske fag | nb_NO |