  In the context of computer models, calibration is the process of estimating
unknown simulator parameters from observational data. Calibration is variously
referred to as model fitting, parameter estimation/inference, an inverse
problem, and model tuning. The need for calibration occurs in most areas of
science and engineering, and has been used to estimate hard to measure
parameters in climate, cardiology, drug therapy response, hydrology, and many
other disciplines. Although the statistical method used for calibration can
vary substantially, the underlying approach is essentially the same and can be
considered abstractly. In this survey, we review the decisions that need to be
taken when calibrating a model, and discuss a range of computational methods
that can be used to compute Bayesian posterior distributions.
