Using a Gaussian Process as a Nonparametric Regression Model

Using a Gaussian Process as a Nonparametric Regression Model

We show how a Gaussian Process (GP) can be used as a nonparametric regression model to fit experiment data that captures the relationship between the experiment response and the experiment factors. We illustrate the GP model analysis with a solar collector computer experiment. We also illustrate how...

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Veröffentlicht in:Quality and reliability engineering international 2016-03, Vol.32 (2), p.673-680
Hauptverfasser: Gattiker, J. R., Hamada, M. S., Higdon, D. M., Schonlau, M., Welch, W. J.
Format: Artikel
Sprache:eng
Schlagworte:
Bayesian
computer experiment
main and joint effects
optimization
physical experiment
prediction
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Zusammenfassung:We show how a Gaussian Process (GP) can be used as a nonparametric regression model to fit experiment data that captures the relationship between the experiment response and the experiment factors. We illustrate the GP model analysis with a solar collector computer experiment. We also illustrate how physical experiment data can be analyzed using a GP. Copyright © 2015 John Wiley & Sons, Ltd.
ISSN:0748-8017
1099-1638
DOI:10.1002/qre.1782