25 citations · 25 across the 3 of their papers we have counts for
5 papers
Penalising model component complexity: A principled, practical approach to constructing priors
Daniel P. Simpson, Håvard Rue, Thiago G. Martins +2
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 c…
Sensitivity analysis for Bayesian hierarchical models
Malgorzata Roos, Thiago G. Martins, Leonhard Held +1
Prior sensitivity examination plays an important role in applied Bayesian analyses. This is especially true for Bayesian hierarchical models, where interpretability of the paramete…
Inference on Dynamic Models for non-Gaussian Random Fields using INLA: A Homicide Rate Analysis of Brazilian Cities
Renan Xavier Cortes, Thiago Guerrera Martins, Marcos Oliveira Prates +1
Robust time series analysis is an important subject in statistical modeling. Models based on Gaussian distribution are sensitive to outliers, which may imply in a significant degra…
Extending INLA to a class of near-Gaussian latent models
Thiago G. Martins, Håvard Rue
This work extends the Integrated Nested Laplace Approximation (INLA) method to latent models outside the scope of latent Gaussian models, where independent components of the latent…
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…