  This article studies Bayesian model averaging (BMA) in the context of
competing expensive computer models in a typical nuclear physics setup. While
it is well known that BMA accounts for the additional uncertainty of the model
itself, we show that it also decreases the posterior variance of the prediction
errors via an explicit decomposition. We extend BMA to the situation where the
competing models are defined on non-identical study regions. Any model's local
forecasting difficulty is offset by predictions obtained from the average
model, thus extending individual models to the full domain. We illustrate our
methodology via pedagogical simulations and applications to forecasting nuclear
observables, which exhibit convincing improvements in both the BMA prediction
error and empirical coverage probabilities.
