Bayesian computing with INLA: New features

Thiago G. Martins; Daniel Simpson; Finn Lindgren; Håvard Rue

doi:10.1016/j.csda.2013.04.014

Bayesian computing with INLA: New features

Thiago G. Martins^*, Daniel Simpson, Finn Lindgren, Håvard Rue

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

386 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	68-83
Number of pages	16
Journal	Computational Statistics and Data Analysis
Volume	67
DOIs	https://doi.org/10.1016/j.csda.2013.04.014
State	Published - 2013
Externally published	Yes

Keywords

Approximate
Bayesian inference
INLA
Latent Gaussian models

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/j.csda.2013.04.014

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title = "Bayesian computing with INLA: New features",

abstract = "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.",

keywords = "Approximate, Bayesian inference, INLA, Latent Gaussian models",

author = "Martins, {Thiago G.} and Daniel Simpson and Finn Lindgren and H{\aa}vard Rue",

year = "2013",

doi = "10.1016/j.csda.2013.04.014",

language = "English (US)",

volume = "67",

pages = "68--83",

journal = "Computational Statistics and Data Analysis",

issn = "0167-9473",

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TY - JOUR

T1 - Bayesian computing with INLA

T2 - New features

AU - Martins, Thiago G.

AU - Simpson, Daniel

AU - Lindgren, Finn

AU - Rue, Håvard

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Approximate

KW - Bayesian inference

KW - INLA

KW - Latent Gaussian models

UR - http://www.scopus.com/inward/record.url?scp=84878373364&partnerID=8YFLogxK

U2 - 10.1016/j.csda.2013.04.014

DO - 10.1016/j.csda.2013.04.014

M3 - Article

AN - SCOPUS:84878373364

SN - 0167-9473

VL - 67

SP - 68

EP - 83

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

ER -

Bayesian computing with INLA: New features

Abstract

Keywords

ASJC Scopus subject areas

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