Abstract

Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in physics, engineering, biology and other disciplines. The utility of these models depends on how well they are calibrated to empirical data. Their calibration is hindered however, both by large numbers of input and output parameters and by run times that increase with the model’s complexity. In this talk we present a calibration method called History Matching, which is iterative and scales well with the dimensionality of the problem. History matching is based on the concept of an emulator, which is a Bayesian representation of our beliefs about the model, given the runs that are available to us. Capitalising on the efficiency of the emulator, History Matching iteratively discards regions of the input space that are unlikely to provide a good match to the empirical data, and is based on successive runs of the computer model in narrowing areas of the input space, which are known as waves. This calibration technique can be embedded in a comprehensive error modelling framework, that takes into account various sources of uncertainty, due to the parameters, the model itself, the observations etc. A calibration example of a high dimensional HIV model will be used to illustrate the method.