Tony O'Hagan - Academic pages - Abstracts

## Bayesian Uncertainty Analysis and Radiological Protection

Richard Haylock and Anthony O'Hagan

*University of Nottingham*

**Publication details: **
In *Statistics for the Environment 3, Pollution Assessment
and Control*, 109-128. V. Barnett and K. F. Turkman (eds.).
Wiley: Chichester, 1997.

### Abstract

Complex mathematical models are used in many disciplines and
notably in environmental modelling. They are often implemented
in large computer programs that may take massive computing power
to run. Typically, the necessary inputs of the model are not
known exactly but are expressed via (often subjective) probability
distributions. The user then wishes to know how this uncertainty
about the inputs translates into uncertainty about the outputs.
This problem is known as uncertainty analysis.

The usual approach is some form of Monte Carlo analysis, sampling
values of the inputs, running the program for each and thereby
obtaining a sample of outputs. But with large models it may not
be possible to do enough runs to get an adequate sample. This
paper will present a Bayesian approach to uncertainty analysis
which can achieve much better results from dramatically fewer
runs of the computer program. Examples are given in the field
of radiological protection.

Return to my publications page.

Updated: 17 February 2000
Maintained by: Tony O'Hagan