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Title: 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:
 [1];  [2]
  1. Univ. of Michigan, Ann Arbor, MI (United States)
  2. Georgia Inst. of Technology, Atlanta, GA (United States)
Publication Date:
Research Org.:
Georgia Tech Research Corp., Atlanta, GA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1405183
Report Number(s):
DOE-GT-0010548-8
Journal ID: ISSN 1017-0405; FG02-13ER26159
Grant/Contract Number:  
SC0010548
Resource Type:
Journal Article: 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. doi:10.5705/ss.202015.0404.
Plumlee, Matthew, and Joseph, V. Roshan. Sun . "Orthogonal Gaussian process models". United States. doi: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}
}

Journal Article:
Free Publicly Available Full Text
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