Browsing NTNU Open by Author "Lindgren, Finn"

Now showing items 1-5 of 5

    • Constructing Priors that Penalize the Complexity of Gaussian Random Fields 

      Fuglstad, Geir-Arne; Simpson, Daniel; Lindgren, Finn; Rue, Håvard (Journal article; Peer reviewed, 2018)
      Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance ...

    • Hierarchical Modelling of Haplotype Effects on a Phylogeny 

      Selle, Maria Lie; Steinsland, Ingelin; Lindgren, Finn; Brajkovic, Vladimir; Cubric-Curik, Vlatka; Gorjanc, Gregor (Peer reviewed; Journal article, 2020)
      We introduce a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are many, but not all possible, ...

    • Quantile based modeling of diurnal temperature range with the five-parameter lambda distribution 

      Vandeskog, Silius Mortensønn; Thorarinsdottir, Thordis Linda; Steinsland, Ingelin; Lindgren, Finn (Peer reviewed; Journal article, 2022)
      Diurnal temperature range is an important variable in climate science that canprovide information regarding climate variability and climate change. Changesindiurnaltemperaturerangecanhaveimplicationsforhydrology,humanhealthand ...

    • Spatial Modelling and Inference with SPDE-based GMRFs 

      Fuglstad, Geir-Arne (Master thesis, 2011)
      In recent years, stochastic partial differential equations (SPDEs) have been shown to provide a usefulway of specifying some classes of Gaussian random fields. The use of an SPDEallows for the construction of a Gaussian ...

    • Spatial modelling with R-INLA: A review 

      Bakka, Haakon; Rue, Håvard; Fuglstad, Geir-Arne; Riebler, Andrea Ingeborg; Bolin, David; Illian, Janine B.; Krainski, Elias Teixeira; Simpson, Daniel; Lindgren, Finn (Journal article; Peer reviewed, 2018)
      Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically‐sized datasets from scratch is ...