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Expected-posterior prior distributions for model selection

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  • Jose M. Perez

Abstract

We consider the problem of comparing parametric models using a Bayesian approach. A new method of developing prior distributions for the model parameters is presented, called the expected-posterior prior approach. The idea is to define the priors for all models from a common underlying predictive distribution, in such a way that the resulting priors are amenable to modern Markov chain Monte Carlo computational techniques. The approach has subjective Bayesian and default Bayesian implementations, and overcomes the most significant impediment to Bayesian model selection, that of ensuring that prior distributions for the various models are appropriately compatible. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • Jose M. Perez, 2002. "Expected-posterior prior distributions for model selection," Biometrika, Biometrika Trust, vol. 89(3), pages 491-512, August.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:3:p:491-512
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    Cited by:

    1. Mulder, Joris, 2014. "Prior adjusted default Bayes factors for testing (in)equality constrained hypotheses," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 448-463.
    2. Ando, Tomohiro & Tsay, Ruey, 2010. "Predictive likelihood for Bayesian model selection and averaging," International Journal of Forecasting, Elsevier, vol. 26(4), pages 744-763, October.
    3. Andrew J. Womack & Luis León-Novelo & George Casella, 2014. "Inference From Intrinsic Bayes' Procedures Under Model Selection and Uncertainty," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1040-1053, September.
    4. J. Cano & D. Salmerón & C. Robert, 2008. "Integral equation solutions as prior distributions for Bayesian model selection," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 493-504, November.
    5. Davide Altomare & Guido Consonni & Luca La Rocca, 2011. "Objective Bayesian Search of Gaussian DAG Models with Non-local Priors," Quaderni di Dipartimento 140, University of Pavia, Department of Economics and Quantitative Methods.
    6. N. Friel & A. N. Pettitt, 2008. "Marginal likelihood estimation via power posteriors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 589-607, July.
    7. Guido Consonni & Monia Lupparelli, 2009. "Bayesian model comparison based on expected posterior priors for discrete decomposable graphical models," Quaderni di Dipartimento 095, University of Pavia, Department of Economics and Quantitative Methods.

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