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Bayesian computing with INLA: New features

Author

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  • Martins, Thiago G.
  • Simpson, Daniel
  • Lindgren, Finn
  • Rue, Håvard

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.

Suggested Citation

  • Martins, Thiago G. & Simpson, Daniel & Lindgren, Finn & Rue, Håvard, 2013. "Bayesian computing with INLA: New features," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 68-83.
  • Handle: RePEc:eee:csdana:v:67:y:2013:i:c:p:68-83
    DOI: 10.1016/j.csda.2013.04.014
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    References listed on IDEAS

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    1. Jo Eidsvik & Sara Martino & Håvard Rue, 2009. "Approximate Bayesian Inference in Spatial Generalized Linear Mixed Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 1-22, March.
    2. Sara Martino & Rupali Akerkar & Håvard Rue, 2011. "Approximate Bayesian Inference for Survival Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(3), pages 514-528, September.
    3. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    4. Birgit Schrödle & Leonhard Held & Andrea Riebler & Jürg Danuser, 2011. "Using integrated nested Laplace approximations for the evaluation of veterinary surveillance data from Switzerland: a case‐study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(2), pages 261-279, March.
    5. Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
    6. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    7. Guo X. & Carlin B.P., 2004. "Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages," The American Statistician, American Statistical Association, vol. 58, pages 16-24, February.
    8. Yue, Yu Ryan & Rue, Håvard, 2011. "Bayesian inference for additive mixed quantile regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 84-96, January.
    9. Sara Martino & Kjersti Aas & Ola Lindqvist & Linda Neef & Håvard Rue, 2011. "Estimating stochastic volatility models using integrated nested Laplace approximations," The European Journal of Finance, Taylor & Francis Journals, vol. 17(7), pages 487-503.
    10. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    11. Hosseini, Fatemeh & Eidsvik, Jo & Mohammadzadeh, Mohsen, 2011. "Approximate Bayesian inference in spatial GLMM with skew normal latent variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1791-1806, April.
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    3. repec:eee:ecomod:v:291:y:2014:i:c:p:175-181 is not listed on IDEAS
    4. repec:spr:empeco:v:56:y:2019:i:3:d:10.1007_s00181-017-1372-9 is not listed on IDEAS
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    6. repec:bla:biomet:v:73:y:2017:i:1:p:242-251 is not listed on IDEAS
    7. Anna Gottard & Giorgio Calzolari, 2014. "Alternative estimating procedures for multiple membership logit models with mixed effects: indirect inference and data cloning," Econometrics Working Papers Archive 2014_07, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    8. Petter Arnesen & Håkon Tjelmeland, 2015. "Fully Bayesian Binary Markov Random Field Models: Prior Specification and Posterior Simulation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 967-987, December.
    9. Thiago G. Martins & Håvard Rue, 2014. "Extending Integrated Nested Laplace Approximation to a Class of Near-Gaussian Latent Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 893-912, December.
    10. repec:bla:jorssa:v:181:y:2018:i:3:p:783-802 is not listed on IDEAS
    11. repec:spr:demogr:v:56:y:2019:i:1:d:10.1007_s13524-018-0737-9 is not listed on IDEAS
    12. Christian P. Robert, 2013. "Bayesian Computational Tools," Working Papers 2013-45, Center for Research in Economics and Statistics.
    13. repec:bla:jtsera:v:38:y:2017:i:6:p:923-935 is not listed on IDEAS

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