• 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 ...
    • Estimating under-five mortality in space and time in a developing world context 

      Wakefield, Jon; Fuglstad, Geir-Arne; Riebler, Andrea Ingeborg; Godwin, Jessica; Wilson, Katie; Clark, Samuel J. (Journal article; Peer reviewed, 2018)
      Accurate estimates of the under-five mortality rate in a developing world context are a key barometer of the health of a nation. This paper describes a new model to analyze survey data on mortality in this context. We are ...
    • Intuitive Joint Priors for Variance Parameters 

      Fuglstad, Geir-Arne; Hem, Ingeborg Gullikstad; Knight, Alexander; Rue, Håvard; Riebler, Andrea Ingeborg (Journal article; Peer reviewed, 2019)
      Variance parameters in additive models are typically assigned independent priors that do not account for model structure. We present a new framework for prior selection based on a hierarchical decomposition of the total ...
    • meta4diag: Bayesian bivariate meta-analysis of diagnostic test studies for routine practice 

      Guo, Jingyi; Riebler, Andrea Ingeborg (Journal article; Peer reviewed, 2017)
      This paper introduces the R package meta4diag for implementing Bayesian bivariate meta-analyses of diagnostic test studies. Our package meta4diag is a purpose-built front end of the R package INLA. While INLA offers full ...
    • 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 gender-age-period-cohort analysis of pancreatic cancer mortality in Spain (1990-2013) 

      Etxeberria, Jaione; Goicoa, Tomás; López-Abente, Gonzalo; Riebler, Andrea Ingeborg; Ugarte, Maria Dolores (Journal article; Peer reviewed, 2017)
      Recently, the interest in studying pancreatic cancer mortality has increased due to its high lethality. In this work a detailed analysis of pancreatic cancer mortality in Spanish provinces was performed using recent data. ...
    • 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 ...
    • 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 ...