Uncertainty Analysis and other Inference Tools for Complex Computer Codes

Abstract

Abstract This paper builds on work by Haylock and O’Hagan which developed a Bayesian approach to uncertainty analysis. The generic problem is to make posterior inference about the output of a complex computer code, and the specific problem of uncertainty analysis is to make inference when the “true” values of the input parameters are unknown. Given the distribution of the input parameters (which is often a subjective distribution derived from expert opinion), we wish to make inference about the implied distribution of the output. The computer code is sufficiently complex that the time to compute the output for any input configuration is substantial. The Bayesian approach was shown to improve dramatically on the classical approach, which is based on drawing a sample of values of the input parameters and thereby obtaining a sample from the output distribution. We review the basic Bayesian approach to the generic problem of inference for complex computer codes, and present some recent advances—inference about the distribution of quantile functions of the uncertainty distribution, calibration of models, and the use of runs of the computer code at different levels of complexity to make efficient use of the quicker, cruder, versions of the code. The emphasis is on practical applications.