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The Varying Success of Bayesian Model Averaging: An Empirical Study of Flood Prediction

Bayesian model averaging attempts to find the most probable prediction, in contrast to finding the prediction of the single best-fit, most probable model. Using an example problem of predicting floods in five intermittent rivers in the Australian outback, this paper establishes that the improvement in prediction accuracy from Bayesian model averaging correlates with the size of the difference in the prediction between Bayesian model averaging and the single best-fit model.