Orthogonal Gaussian process models
Abstract
Gaussian processes models are widely adopted for nonparameteric/semi-parametric modeling. Identifiability issues occur when the mean model contains polynomials with unknown coefficients. Though resulting prediction is unaffected, this leads to poor estimation of the coefficients in the mean model, and thus the estimated mean model loses interpretability. This paper introduces a new Gaussian process model whose stochastic part is orthogonal to the mean part to address this issue. As a result, this paper also discusses applications to multi-fidelity simulations using data examples.
- Authors:
-
- Univ. of Michigan, Ann Arbor, MI (United States)
- Georgia Inst. of Technology, Atlanta, GA (United States)
- Publication Date:
- Research Org.:
- Georgia Institute of Technology, Atlanta, GA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI Identifier:
- 1405183
- Report Number(s):
- DOE-GT-0010548-8
Journal ID: ISSN 1017-0405; FG02-13ER26159
- Grant/Contract Number:
- SC0010548
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Statistica Sinica
- Additional Journal Information:
- Journal Volume: 28; Journal ID: ISSN 1017-0405
- Publisher:
- Institute of Statistical Science, Academia Sinica - International Chinese Statistical Association
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Computer experiments; Identifiability; Kriging; Multi-fidelity simulations; Universal kriging
Citation Formats
Plumlee, Matthew, and Joseph, V. Roshan. Orthogonal Gaussian process models. United States: N. p., 2017.
Web. doi:10.5705/ss.202015.0404.
Plumlee, Matthew, & Joseph, V. Roshan. Orthogonal Gaussian process models. United States. https://doi.org/10.5705/ss.202015.0404
Plumlee, Matthew, and Joseph, V. Roshan. Sun .
"Orthogonal Gaussian process models". United States. https://doi.org/10.5705/ss.202015.0404. https://www.osti.gov/servlets/purl/1405183.
@article{osti_1405183,
title = {Orthogonal Gaussian process models},
author = {Plumlee, Matthew and Joseph, V. Roshan},
abstractNote = {Gaussian processes models are widely adopted for nonparameteric/semi-parametric modeling. Identifiability issues occur when the mean model contains polynomials with unknown coefficients. Though resulting prediction is unaffected, this leads to poor estimation of the coefficients in the mean model, and thus the estimated mean model loses interpretability. This paper introduces a new Gaussian process model whose stochastic part is orthogonal to the mean part to address this issue. As a result, this paper also discusses applications to multi-fidelity simulations using data examples.},
doi = {10.5705/ss.202015.0404},
journal = {Statistica Sinica},
number = ,
volume = 28,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2017},
month = {Sun Jan 01 00:00:00 EST 2017}
}
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Cited by: 8 works
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