Inferences in Bayesian variable selection problems with large model spaces

An important aspect of Bayesian model selection is how to deal with huge model spaces, since exhaustive enumeration of all the models entertained is unfeasible and inferences have to be based on the very small proportion of models visited. This is the case for the variable selection problem, with a moderate to large number of possible explanatory variables being considered in this paper. We review some of the strategies proposed in the literature and argue that inferences based on empirical frequencies via Markov Chain Monte Carlo sampling of the posterior distribution outperforms recently proposed searching methods. We give a plausible yet very simple explanation of this effect, showing that estimators based on frequencies are unbiased. The results obtained in two illustrative examples provide strong evidence in favor of our arguments.

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