Computer codes are used in scientific research to study and predict the behaviour of complex systems. Their run times often make uncertainty and sensitivity analyses impractical because of the thousands of runs that are conventionally required, so efficient techniques have been developed based on a statistical representation of the code. The approach is less straightforward for dynamic codes, which represent time-evolving systems. We develop a novel iterative system to build a statistical model of dynamic computer codes, which is demonstrated on a rainfall-runoff simulator.
|Number of pages||14|
|Publication status||Published - Sept 2009|
Bibliographical noteFunding Information:
The work in this paper is part of the activities of the Managing Uncertainty in Complex Models project that is funded by a Research Councils UK grant. We thank Peter Reichert for providing the details of the rainfall-runoff model analyzed in this paper. We would also like to thank Professor D. M. Titterington and the anonymous referees for their helpful and stimulating comments on earlier drafts of this paper.
Copyright 2009 Elsevier B.V., All rights reserved.
- Bayesian inference
- Computer experiment
- Dynamic simulator
- Gaussian process
- Iterative modelling