Prior-based Bayesian information criterion
| Autor: | James O. Berger, Luis R. Pericchi, Maria J. Bayarri, Ingmar Visser, Woncheol Jang, Surajit Ray |
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| Přispěvatelé: | Ontwikkelingspsychologie (Psychologie, FMG) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Laplace expansion Applied Mathematics Bayes factor Marginal likelihood Statistics::Computation symbols.namesake Computational Theory and Mathematics Laplace's method Bayesian information criterion Prior probability symbols Applied mathematics Statistics::Methodology Statistics Probability and Uncertainty Likelihood function Fisher information Analysis Mathematics |
| Zdroj: | Statistical Theory and Related Fields, 3(1), 2-13. Routledge |
| ISSN: | 2475-4269 |
| Popis: | We present a new approach to model selection and Bayes factor determination, based on Laplace expansions (as in BIC), which we call Prior-based Bayes Information Criterion (PBIC). In this approach, the Laplace expansion is only done with the likelihood function, and then a suitable prior distribution is chosen to allow exact computation of the (approximate) marginal likelihood arising from the Laplace approximation and the prior. The result is a closed-form expression similar to BIC, but now involves a term arising from the prior distribution (which BIC ignores) and also incorporates the idea that different parameters can have different effective sample sizes (whereas BIC only allows one overall sample size n). We also consider a modification of PBIC which is more favourable to complex models. |
| Databáze: | OpenAIRE |
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