Bayesian model averaging with the integrated nested laplace approximation
KAUST DepartmentStatistics Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2019-11-02
Permanent link to this recordhttp://hdl.handle.net/10754/660707
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AbstractThe 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.
CitationGómez-Rubio, V., Bivand, R. S., & Rue, H. (2020). Bayesian Model Averaging with the Integrated Nested Laplace Approximation. Econometrics, 8(2), 23. doi:10.3390/econometrics8020023
SponsorsVirgilio 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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