Blackwell Publishing
Oxford, UK
RSSB Journal of the Royal Statistical Society Series B 1369-7412 2004 Royal Statistical Society
August 2004 66 3 751 769 Original Articles Probabilistic Sensitivity Analysis J. E. Oakley and A. O'Hagan

[Received May 2002. Revised December 2003]

Jeremy E. Oakley, Department of Probability and Statistics, University of Sheffield, Sheffield, S3 7RH, UK.
E-mail: j.oakley@sheffield.ac.uk
Probabilistic sensitivity analysis of complex models: a Bayesian approach Jeremy E. Oakley 1 and Anthony O'Hagan 1
1 University of Sheffield, UK
Summary.

In many areas of science and technology, mathematical models are built to simulate complex real world phenomena. Such models are typically implemented in large computer programs and are also very complex, such that the way that the model responds to changes in its inputs is not transparent. Sensitivity analysis is concerned with understanding how changes in the model inputs influence the outputs. This may be motivated simply by a wish to understand the implications of a complex model but often arises because there is uncertainty about the true values of the inputs that should be used for a particular application. A broad range of measures have been advocated in the literature to quantify and describe the sensitivity of a model's output to variation in its inputs. In practice the most commonly used measures are those that are based on formulating uncertainty in the model inputs by a joint probability distribution and then analysing the induced uncertainty in outputs, an approach which is known as probabilistic sensitivity analysis. We present a Bayesian framework which unifies the various tools of prob- abilistic sensitivity analysis. The Bayesian approach is computationally highly efficient. It allows effective sensitivity analysis to be achieved by using far smaller numbers of model runs than standard Monte Carlo methods. Furthermore, all measures of interest may be computed from a single set of runs.

Keywords:  Bayesian inference; Computer model; Gaussian process; Sensitivity analysis; Uncertainty analysis