Using a Gaussian Process as a Nonparametric Regression Model | Zendy

Gattiker J. R. | Zendy; Hamada M. S. | Zendy; Higdon D. M. | Zendy; Schonlau M. | Zendy; Welch W. J. | Zendy

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Using a Gaussian Process as a Nonparametric Regression Model

Author(s) -

Gattiker J. R.,

Hamada M. S.,

Higdon D. M.,

Schonlau M.,

Welch W. J.

Publication year - 2016

Publication title -

quality and reliability engineering international

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.913

H-Index - 62

eISSN - 1099-1638

pISSN - 0748-8017

DOI - 10.1002/qre.1782

Subject(s) - nonparametric statistics , nonparametric regression , gaussian process , kriging , regression analysis , computer science , regression , statistics , process (computing) , artificial intelligence , econometrics , gaussian , machine learning , mathematics , physics , quantum mechanics , operating system

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.

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