• 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 ...
    • Modeling Spatial Dependencies using Barriers and Different Terrains 

      Bakka, Haakon (Doctoral theses at NTNU;2017:69, Doctoral thesis, 2017)
    • Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors 

      Simpson, Daniel; Rue, Håvard; Riebler, Andrea Ingeborg; Martins, Thiago Guerrera; Sørbye, Sigrunn Holbek (Journal article; Peer reviewed, 2017)
      In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a ...
    • 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 ...
    • Stochastic Models for Smoothing Splines: A Bayesian Approach 

      Hellton, Kristoffer Herland (Master thesis, 2011)
      Flexible data regression is an important tool for capturing complicated trends in data. One approach is penalized smoothing splines, where there are several mainstream methods. A weakness is, however, the quantification ...
    • You Just Keep on Pushing My Love over the Borderline: A Rejoinder 

      Simpson, Daniel; Rue, Håvard; Riebler, Andrea Ingeborg; Martins, Thiago Guerrera; Sørbye, Sigrunn Holbek (Journal article; Peer reviewed, 2017)
      The entire reason that we wrote this paper was to provide a concrete object around which to focus a broader discussion about prior choice and we are extremely grateful to the editorial team at Statistical Science for this ...