Local Gaussian Process Approximation for Large Computer Experiments

Robert B. Gramacy, Daniel W. Apley

Research output: Contribution to journalArticlepeer-review

95 Scopus citations

Abstract

We provide a new approach to approximate emulation of large computer experiments. By focusing expressly on desirable properties of the predictive equations, we derive a family of local sequential design schemes that dynamically define the support of a Gaussian process predictor based on a local subset of the data. We further derive expressions for fast sequential updating of all needed quantities as the local designs are built up iteratively. Then we show how independent application of our local design strategy across the elements of a vast predictive grid facilitates a trivially parallel implementation. The end result is a global predictor able to take advantage of modern multicore architectures, providing a nonstationary modeling feature as a bonus. We demonstrate our method on two examples using designs with thousands of data points, and compare to the method of compactly supported covariances. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)561-578
Number of pages18
JournalJournal of Computational and Graphical Statistics
Volume24
Issue number2
DOIs
StatePublished - Apr 3 2015

Keywords

  • Active learning
  • Compactly supported
  • Covariance
  • Emulator
  • Local kriging neighborhoods
  • Sequential design
  • Sequential updating
  • Surrogate model

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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