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dc.contributor.advisorSteinsland, Ingelinnb_NO
dc.contributor.authorBøhn, Eirik Dybviknb_NO
dc.date.accessioned2014-12-19T14:00:28Z
dc.date.available2014-12-19T14:00:28Z
dc.date.created2014-06-27nb_NO
dc.date.issued2014nb_NO
dc.identifier730496nb_NO
dc.identifierntnudaim:11085nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/259302
dc.description.abstractIn this study we focus on performing inference on bivariate animal models using Integrated Nested Laplace Approximation (INLA). INLA is a methodology for making fast non-sampling based Bayesian inference for hierarchical Gaussian Markov models. Animal models are generalized mixed models (GLMM) used in evolutionary biology and animal breeding to identify the genetic part of traits. Bivariate animal models are derived and shown to fit the INLA framework. Simulation studies are conducted to evaluate the performance of the models. The models are fitted to a real data set of Scots pine to investigate correlations and dependencies.nb_NO
dc.languageengnb_NO
dc.publisherInstitutt for matematiske fagnb_NO
dc.titleModelling and Inference for Bayesian Bivariate Animal Models using Integrated Nested Laplace Approximationsnb_NO
dc.typeMaster thesisnb_NO
dc.source.pagenumber58nb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for matematiske fagnb_NO


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