  To improve the predictability of complex computational models in the
experimentally-unknown domains, we propose a Bayesian statistical machine
learning framework utilizing the Dirichlet distribution that combines results
of several imperfect models. This framework can be viewed as an extension of
Bayesian stacking. To illustrate the method, we study the ability of Bayesian
model averaging and mixing techniques to mine nuclear masses. We show that the
global and local mixtures of models reach excellent performance on both
prediction accuracy and uncertainty quantification and are preferable to
classical Bayesian model averaging. Additionally, our statistical analysis
indicates that improving model predictions through mixing rather than mixing of
corrected models leads to more robust extrapolations.
