Bayesian Model Averaging with the Integrated Nested Laplace Approximation
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 represent...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Econometrics |
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| Online Access: | https://www.mdpi.com/2225-1146/8/2/23 |
| _version_ | 1827715780714692608 |
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| author | Virgilio Gómez-Rubio Roger S. Bivand Håvard Rue |
| author_facet | Virgilio Gómez-Rubio Roger S. Bivand Håvard Rue |
| author_sort | Virgilio Gómez-Rubio |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:26:45Z |
| format | Article |
| id | doaj.art-6da3a7dbbc444c60900f9957a4460b29 |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-03-10T19:26:45Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-6da3a7dbbc444c60900f9957a4460b292023-11-20T02:30:28ZengMDPI AGEconometrics2225-11462020-06-01822310.3390/econometrics8020023Bayesian Model Averaging with the Integrated Nested Laplace ApproximationVirgilio Gómez-Rubio0Roger S. Bivand1Håvard Rue2Department of Mathematics, School of Industrial Engineering, Universidad de Castilla-La Mancha, E-02071 Albacete, SpainDepartment of Economics, Norwegian School of Economics, 5045 Bergen, NorwayCEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi ArabiaThe 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.https://www.mdpi.com/2225-1146/8/2/23Bayesian model averagingINLAspatial econometrics |
| spellingShingle | Virgilio Gómez-Rubio Roger S. Bivand Håvard Rue Bayesian Model Averaging with the Integrated Nested Laplace Approximation Econometrics Bayesian model averaging INLA spatial econometrics |
| title | Bayesian Model Averaging with the Integrated Nested Laplace Approximation |
| title_full | Bayesian Model Averaging with the Integrated Nested Laplace Approximation |
| title_fullStr | Bayesian Model Averaging with the Integrated Nested Laplace Approximation |
| title_full_unstemmed | Bayesian Model Averaging with the Integrated Nested Laplace Approximation |
| title_short | Bayesian Model Averaging with the Integrated Nested Laplace Approximation |
| title_sort | bayesian model averaging with the integrated nested laplace approximation |
| topic | Bayesian model averaging INLA spatial econometrics |
| url | https://www.mdpi.com/2225-1146/8/2/23 |
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