Bayesian model averaging with the integrated nested laplace approximation

Virgilio Gómez-Rubio, Roger S. Bivand, Haavard Rue

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hierarchical models that can be expressed as latent Gaussian Markov random fields (GMRF). The representation as a GMRF allows the associated software R-INLA to estimate the posterior marginals in a fraction of the time as typical Markov chain Monte Carlo algorithms. INLA can be extended by means of Bayesian model averaging (BMA) to increase the number of models that it can fit to conditional latent GMRF. In this paper, we review the use of BMA with INLA and propose a new example on spatial econometrics models.
Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalEconometrics
Volume8
Issue number2
DOIs
StatePublished - Jun 2 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Virgilio Gómez-Rubio was funded by Consejería de Educación, Cultura y Deportes (JCCM, Spain) and FEDER, Grant Number SBPLY/17/180501/000491, as well as by Ministerio de Economía y Competitividad (Spain), Grant Number MTM2016-77501-P.

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