We consider the specification of prior distributions for Bayesian model comparison, focusing on regression-type models. We propose a particular joint specification of the prior distribution across models so that sensitivity of posterior model probabilities to the dispersion of prior distributions for the parameters of individual models (Lindley's paradox) is diminished. We illustrate the behavior of inferential and predictive posterior quantities in linear and log-linear regressions under our proposed prior densities with a series of simulated and real data examples.
Keywords: Bayesian inference, BIC, Generalised linear models, Lindley's Paradox, Model averaging,
Regression models.
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First version, 15/3/2008 with title "Specification of prior distributions under model uncertainty".
Presentation for Greek Stochastics delta (25-28 August 2012, Kalamata, Greece): Presentation & Handouts (2 per page) [added 23/8/2012]
