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Screening traditionally refers to the problem of detecting active inputs inthe computer model. In this paper, we develop methodology that applies toscreening, but the main focus is on detecting active inputs not in the computermodel itself but rather on the discrepancy function that is introduced toaccount for model inadequacy when linking the computer model with fieldobservations. We contend this is an important problem as it informs the modelerwhich are the inputs that are potentially being mishandled in the model, butalso along which directions it may be less recommendable to use the model forprediction. The methodology is Bayesian and is inspired by the continuous spikeand slab prior popularized by the literature on Bayesian variable selection. Inour approach, and in contrast with previous proposals, a single MCMC samplefrom the full model allows us to compute the posterior probabilities of all thecompeting models, resulting in a methodology that is computationally very fast.The approach hinges on the ability to obtain posterior inclusion probabilitiesof the inputs, which are very intuitive and easy to interpret quantities, asthe basis for selecting active inputs. For that reason, we name the methodologyPIPS -- posterior inclusion probability screening.

Screening the Discrepancy Function of a Computer Model