Artikel
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 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.
- Sprache
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Englisch
- Erschienen in
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 8 ; Year: 2020 ; Issue: 2 ; Pages: 1-15 ; Basel: MDPI
- Klassifikation
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Wirtschaft
- Thema
-
Bayesian model averaging
INLA
spatial econometrics
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Gómez-Rubio, Virgilio
Bivand, Roger
Rue, Håvard
- Ereignis
-
Veröffentlichung
- (wer)
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MDPI
- (wo)
-
Basel
- (wann)
-
2020
- DOI
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doi:10.3390/econometrics8020023
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Gómez-Rubio, Virgilio
- Bivand, Roger
- Rue, Håvard
- MDPI
Entstanden
- 2020
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