  Screening traditionally refers to the problem of detecting active inputs in
the computer model. In this paper, we develop methodology that applies to
screening, but the main focus is on detecting active inputs not in the computer
model itself but rather on the discrepancy function that is introduced to
account for model inadequacy when linking the computer model with field
observations. We contend this is an important problem as it informs the modeler
which are the inputs that are potentially being mishandled in the model, but
also along which directions it may be less recommendable to use the model for
prediction. The methodology is Bayesian and is inspired by the continuous spike
and slab prior popularized by the literature on Bayesian variable selection. In
our approach, and in contrast with previous proposals, a single MCMC sample
from the full model allows us to compute the posterior probabilities of all the
competing models, resulting in a methodology that is computationally very fast.
The approach hinges on the ability to obtain posterior inclusion probabilities
of the inputs, which are very intuitive and easy to interpret quantities, as
the basis for selecting active inputs. For that reason, we name the methodology
PIPS -- posterior inclusion probability screening.
