• 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 ...
    • 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 ...