Orthogonal Gaussian process models

Journal Article · Sun Jan 01 00:00:00 EST 2017 · Statistica Sinica

DOI:https://doi.org/10.5705/ss.202015.0404· OSTI ID:1405183

Plumlee, Matthew ^[1]; Joseph, V. Roshan ^[2]

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1. Univ. of Michigan, Ann Arbor, MI (United States)
2. Georgia Inst. of Technology, Atlanta, GA (United States)

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.

• Research Organization: Georgia Institute of Technology, Atlanta, GA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: SC0010548
• OSTI ID: 1405183
• Report Number(s): DOE-GT-0010548-8; FG02-13ER26159
• Journal Information: Statistica Sinica, Vol. 28; ISSN 1017-0405
• Publisher: Institute of Statistical Science, Academia Sinica - International Chinese Statistical Association
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Computer experiments
Identifiability
Kriging
Multi-fidelity simulations
Universal kriging