Orthogonal Gaussian process models

Matthew Plumlee, V. Roshan Joseph

Research output: Contribution to journalEditorialpeer-review

5 Scopus citations

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. This paper also discusses applications to multi-fidelity simulations using data examples.

Original languageEnglish (US)
Pages (from-to)601-619
Number of pages19
JournalStatistica Sinica
Volume28
Issue number2
DOIs
StatePublished - Apr 2018

Keywords

  • Computer experiments
  • Identifiability
  • Kriging
  • Multi-fidelity simulations
  • Universal kriging

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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