Leitura do Dia - Bayesian computing with INLA: new features
Bayesian computing with INLA: new features
Thiago G. Martins, Daniel Simpson, Finn Lindgren, Håvard Rue
(Submitted on 1 Oct 2012)
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 tool in practice via the R-package R-INLA. In this paper we formalize new developments in the R-INLA package and show how these features greatly extend the scope of models that can be analyzed by this interface. We also discuss the current default method in R-INLA to approximate posterior marginals of the hyperparameters using only a modest number of evaluations of the joint posterior distribution of the hyperparameters, without any need for numerical integration.
Thiago G. Martins, Daniel Simpson, Finn Lindgren, Håvard Rue
(Submitted on 1 Oct 2012)
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 tool in practice via the R-package R-INLA. In this paper we formalize new developments in the R-INLA package and show how these features greatly extend the scope of models that can be analyzed by this interface. We also discuss the current default method in R-INLA to approximate posterior marginals of the hyperparameters using only a modest number of evaluations of the joint posterior distribution of the hyperparameters, without any need for numerical integration.
posted by Márcio Laurini at 10:11 AM
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