Comparing and calibrating discrepancy measures for Bayesian model selection
Different approaches have been considered in the literatur e for the problem of Bayesian model selection. Recently, a new method was introduced and analys ed in De la Horra (2008) by minimizing the posterior expected discrepancy between the set of data and the Bayesian model, where the chi-square di...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/13288 |
| Acceso en línea: | https://hdl.handle.net/2099/13288 |
| Access Level: | acceso abierto |
| Palabra clave: | Mathematical statistics Bayesian model selection Discrepancy measure Calibration Posterior expected discrepancy Estadística matemàtica Classificació AMS::62 Statistics::62F Parametric inference Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Sumario: | Different approaches have been considered in the literatur e for the problem of Bayesian model selection. Recently, a new method was introduced and analys ed 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 th at the chi-square discrepancy is reasonable to use. Then, an easy method for calibrating disc repancies is proposed, and the behaviour of this approach is studied on simulated data. Fin ally, a set of real data is analysed |
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