  A predictive Bayesian model selection approach is presented to discriminate
coupled models used to predict an unobserved quantity of interest (QoI). The
need for accurate predictions arises in a variety of critical applications such
as climate, aerospace and defense. A model problem is introduced to study the
prediction yielded by the coupling of two physics/sub-components. For each
single physics domain, a set of model classes and a set of sensor observations
are available. A goal-oriented algorithm using a predictive approach to
Bayesian model selection is then used to select the combination of single
physics models that best predict the QoI. It is shown that the best coupled
model for prediction is the one that provides the most robust predictive
distribution for the QoI.
