# Model selection with vague prior information

Elias Moreno; F. Javier Girón; M. Lina Martínez

Revista de la Real Academia de Ciencias Exactas Físicas y Naturales (1998)

- Volume: 92, Issue: 4, page 289-298
- ISSN: 1137-2141

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topMoreno, Elias, Girón, F. Javier, and Martínez, M. Lina. "Model selection with vague prior information." Revista de la Real Academia de Ciencias Exactas Físicas y Naturales 92.4 (1998): 289-298. <http://eudml.org/doc/42110>.

@article{Moreno1998,

abstract = {In the Bayesian approach, the Bayes factor is the main tool for model selection and hypothesis testing. When prior information is weak, "default" or "automatic" priors, which are typicaIly improper, are commonly used but, unfortunately, the Bayes factor is defined up to a multiplicative constant. In this paper we revise some recent but already popular methodologies, intrinsic and lractional, to deal with improper priors in model selection and hypothesis testing. Special attention is paid to the intrinsic and fractional methods as tools devised to produce proper priors to compute actual Bayes factors. Sorne illustration to hypothesis testing problems with more than one population are given, in particular the Behrens- Fisher problem is considered.},

author = {Moreno, Elias, Girón, F. Javier, Martínez, M. Lina},

journal = {Revista de la Real Academia de Ciencias Exactas Físicas y Naturales},

language = {eng},

number = {4},

pages = {289-298},

title = {Model selection with vague prior information},

url = {http://eudml.org/doc/42110},

volume = {92},

year = {1998},

}

TY - JOUR

AU - Moreno, Elias

AU - Girón, F. Javier

AU - Martínez, M. Lina

TI - Model selection with vague prior information

JO - Revista de la Real Academia de Ciencias Exactas Físicas y Naturales

PY - 1998

VL - 92

IS - 4

SP - 289

EP - 298

AB - In the Bayesian approach, the Bayes factor is the main tool for model selection and hypothesis testing. When prior information is weak, "default" or "automatic" priors, which are typicaIly improper, are commonly used but, unfortunately, the Bayes factor is defined up to a multiplicative constant. In this paper we revise some recent but already popular methodologies, intrinsic and lractional, to deal with improper priors in model selection and hypothesis testing. Special attention is paid to the intrinsic and fractional methods as tools devised to produce proper priors to compute actual Bayes factors. Sorne illustration to hypothesis testing problems with more than one population are given, in particular the Behrens- Fisher problem is considered.

LA - eng

UR - http://eudml.org/doc/42110

ER -

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