Browsing NTNU Open by Author "Sørbye, Sigrunn Holbek"

Now showing items 1-5 of 5

    • A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA) 

      Illian, Janine; Sørbye, Sigrunn Holbek; Rue, Håvard (Journal article; Peer reviewed, 2012)

    • Bayesian Computing with INLA: A Review 

      Rue, Håvard; Riebler, Andrea Ingeborg; Sørbye, Sigrunn Holbek; Illian, Janine B.; Simpson, Daniel Peter; Lindgren, Finn Kristian (Journal article; Peer reviewed, 2017)
      The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate technique is the Laplace method or approximation, which dates back to Pierre-Simon Laplace (1774). This simple idea ...

    • Modeling Solar Orbiter dust detection rates in the inner heliosphere as a Poisson process 

      Kociscak, Samuel; Kvammen, Andreas; Mann, Ingrid; Sørbye, Sigrunn Holbek; Theodorsen, Audun; Zaslavsky, Arnaud (Peer reviewed; Journal article, 2023)
      Context. Solar Orbiter provides dust detection capability in the inner heliosphere, but estimating physical properties of detected dust from the collected data is far from straightforward. Aims. First, a physical model ...

    • 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 ...

    • 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 ...