11 citations · 18 across the 6 of their papers we have counts for
6 papers
Careful prior specification avoids incautious inference for log-Gaussian Cox point processes
Sigrunn H. Sørbye, Janine B. Illian, Daniel P. Simpson +1
Prior specifications for hyperparameters of random fields in Bayesian spatial point process modelling can have a major impact on the statistical inference and the conclusions made.…
An approximate fractional Gaussian noise model with computational cost
Sigrunn H. Sørbye, Eirik Myrvoll-Nilsen, Håvard Rue
Fractional Gaussian noise (fGn) is a stationary time series model with long memory properties applied in various fields like econometrics, hydrology and climatology. The computatio…
Fast and accurate Bayesian model criticism and conflict diagnostics using R-INLA
Egil Ferkingstad, Leonhard Held, Håvard Rue
Bayesian hierarchical models are increasingly popular for realistic modelling and analysis of complex data. This trend is accompanied by the need for flexible, general, and computa…
A note on intrinsic Conditional Autoregressive models for disconnected graphs
Anna Freni-Sterrantino, Massimo Ventrucci, Håvard Rue
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models sh…
Bayesian computing with INLA: new features
Thiago G. Martins, Daniel Simpson, Finn Lindgren +1
The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable…
Comments on "Particle Markov chain Monte Carlo" by C. Andrieu, A. Doucet, and R. Hollenstein
Pierre Jacob, Nicolas Chopin, Christian P. Robert +1
This is the compilation of our comments submitted to the Journal of the Royal Statistical Society, Series B, to be published within the discussion of the Read Paper of Andrieu, Dou…