  We provide another look at the statistical calibration problem in computer
models. This viewpoint is inspired by two overarching practical considerations
of computer models: (i) many computer models are inadequate for perfectly
modeling physical systems, even with the best-tuned calibration parameters;
(ii) only a finite number of data points are available from the physical
experiment associated with a computer model. Following this new line of
thinking, we provide a non-asymptotic theory and derive a prediction-oriented
calibration method. Our calibration method minimizes the predictive mean
squared error for a finite sample size with statistical guarantees. We
introduce an algorithm to perform the proposed calibration method and connect
it to existing Bayesian calibration methods. Synthetic and real examples are
provided to corroborate the derived theory and illustrate some advantages of
the proposed calibration method.
