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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.