Skip Navigation


Biometrika Advance Access originally published online on June 30, 2009
Biometrika 2009 96(3):663-676; doi:10.1093/biomet/asp028
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Conti, S.
Right arrow Articles by O'Hagan, A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2009 Biometrika Trust

Article

Gaussian process emulation of dynamic computer codes

S. Conti

Centre for Infections, Health Protection Agency, 61 Colindale Ave., London, NW9 5EQ, U.K. stefano.conti{at}hpa.org.uk

J. P. Gosling

Central Science Laboratory, Department for Environment, Food and Rural Affairs, Sand Hutton, York, YO41 1LZ, U.K. jp.gosling{at}csl.gov.uk

J. E. Oakley and A. O'Hagan

Department of Probability and Statistics, University of Sheffield, Sheffield, S3 7RH, U.K. j.oakley{at}sheffield.ac.uk a.ohagan{at}sheffield.ac.uk

Received for publication 1 June 2007. Revision received 1 November 2008.

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.

Key Words: Bayesian inference • Computer experiment • Dynamic simulator • Emulation • Gaussian process • Iterative modelling



References

    Abt M. Estimating the prediction mean squared error in Gaussian stochastic processes with exponential correlation structure. Scand. J. Statist. (1999) 26:563–78.[CrossRef]

    Bhattacharya S. A simulation approach to Bayesian emulation of complex dynamic computer models. Bayesian Anal. (2007) 2:783–816.[CrossRef]

    Cressie N. A. Statistics for Spatial Data. rev. ed. (1993) New York: Wiley.

    Kennedy M., Anderson C., O'Hagan A., Lomas M., Woodward F., Gosling J., Heinemeyer A. Quantifying uncertainty in the biospheric carbon flux for England and Wales. J. R. Statist. Soc. (2008) A 171:109–35.

    Kennedy M., O'Hagan A. Bayesian calibration of computer models (with Discussion). J. R. Statist. Soc. (2001) B 63:425–64.[CrossRef]

    Kuczera G., Kavetski D., Franks S., Thyer M. Towards a Bayesian total error analysis of conceptual rainfall-runoff models: characterising model error using storm-dependent parameters. J. Hydrol. (2006) 331:161–77.[CrossRef]

    Morris M., Mitchell T. Exploratory designs for computer experiments. J. Statist. Plan. Infer. (1995) 43:381–402.[CrossRef]

    Oakley J., O'Hagan A. Bayesian inference for the uncertainty distribution of computer model outputs. Biometrika (2002) 89:769–84.[Abstract/Free Full Text]

    Oakley J. E., O'Hagan A. Probabilistic sensitivity analysis of complex models: a Bayesian approach. J. R. Statist. Soc. (2004) B 66:751–69.[CrossRef]

    O'Hagan A. Bayesian analysis of computer code outputs: a tutorial. Reliab. Eng. Syst. Safety (2006) 91:1290–300.[CrossRef]

    Rougier J. Efficient emulators for multivariate deterministic functions. J. Comp. Graph. Statist. (2008) 17:827–43.[CrossRef]

    Sacks J., Welch W., Mitchell T., Wynn H. Design and analysis of computer experiments. Statist. Sci. (1989) 4:409–23.[CrossRef]

    Saltelli A., Chan K., Scott E. (Eds. Sensitivity Analysis (2000) New York: Wiley.

    Santner T., Williams B., Notz W. The Design and Analysis of Computer Experiments (2003) New York: Springer.

    Strassen V. Gaussian elimination is not optimal. Numer. Math. (1969) 13:354–6.[CrossRef]


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?



This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Conti, S.
Right arrow Articles by O'Hagan, A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?