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Tuned Bayesian Model Averaging

In this paper, we suggest an empirical Bayes-type prior for the model space in Bayesian model averaging (BMA) in a method we call tuned Bayesian model averaging (tBMA). This method relies on leave-one-out cross validation to choose a hyper-parameter that will cause the averaging process to favor either smaller or richer models in the prior distribution over the models. We find that this method can provide significant gains in terms of predictive accuracy, compared to standard BMA. We concentrate on the BIC-approximation of the process, using Occam’s window, which reduces the computational burden.