Comparing and calibrating discrepancy measures for Bayesian model selection
Different approaches have been considered in the literature for the problem of Bayesian model selection. Recently, a new method was introduced and analysed in De la Horra (2008) by minimizing the posterior expected discrepancy between the set of data and the Bayesian model, where the chi-square disc...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:97727 |
| Acceso en línea: | https://ddd.uab.cat/record/97727 |
| Access Level: | acceso abierto |
| Palabra clave: | Bayesian model selection Discrepancy measure Calibration Posterior expected discrepancy |
| Sumario: | Different approaches have been considered in the literature for the problem of Bayesian model selection. Recently, a new method was introduced and analysed in De la Horra (2008) by minimizing the posterior expected discrepancy between the set of data and the Bayesian model, where the chi-square discrepancy was used. In this article, several discrepancy measures are considered and compared by simulation, and it is obtained that the chi-square discrepancy is reasonable to use. Then, an easy method for calibrating discrepancies is proposed, and the behaviour of this approach is studied on simulated data. Finally, a set of real data is analysed. |
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