Bayesian computing with INLA: New features

被引：405

作者：

Martins, Thiago G. ^{[1
]}

Simpson, Daniel ^{[1
]}

Lindgren, Finn ^{[1
]}

Rue, Havard ^{[1
]}

机构：

[1] Norwegian Univ Sci & Technol, Dept Math Sci, N-7491 Trondheim, Norway

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2013年 / 67卷

关键词：

Approximate Bayesian inference; INLA; Latent Gaussian models; NESTED LAPLACE APPROXIMATIONS; LATENT GAUSSIAN MODELS; LINEAR MIXED MODELS; INFERENCE; INFECTIONS;

D O I：

10.1016/j.csda.2013.04.014

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. New developments in the R-INLA are formalized and it is shown how these features greatly extend the scope of models that can be analyzed by this interface. The current default method in R-INLA to approximate the 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, is discussed. (c) 2013 Elsevier B.V. All rights reserved.

引用

页码：68 / 83

页数：16

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