Abstract

Many scientific phenomena are now investigated by complex computer models or codes. A computer experiment is a number of runs of the code with various inputs. A feature of many computer experiments is that the output is deterministic--rerunning the code with the same inputs gives identical observations. Often, the codes are computationally expensive to run, and a common objective of an experiment is to fit a cheaper predictor of the output to the data. Our approach is to model the deterministic output as the realization of a stochastic process, thereby providing a statistical basis for designing experiments (choosing the inputs) for efficient prediction. With this model, estimates of uncertainty of predictions are also available. Recent work in this area is reviewed, a number of applications are discussed, and we demonstrate our methodology with an example.

Keywords

Computer scienceRealization (probability)Code (set theory)Computer experimentSource codeProcess (computing)Feature (linguistics)AlgorithmData miningSimulationStatisticsSet (abstract data type)MathematicsProgramming language

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Publication Info

Year
1989
Type
article
Volume
4
Issue
4
Citations
6923
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Closed

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Jerome Sacks, William J. Welch, Toby J. Mitchell et al. (1989). Design and Analysis of Computer Experiments. Statistical Science , 4 (4) . https://doi.org/10.1214/ss/1177012413

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DOI
10.1214/ss/1177012413