The Annals of Applied Statistics

Composite Gaussian process models for emulating expensive functions

Shan Ba and V. Roshan Joseph

Full-text: Open access

Abstract

A new type of nonstationary Gaussian process model is developed for approximating computationally expensive functions. The new model is a composite of two Gaussian processes, where the first one captures the smooth global trend and the second one models local details. The new predictor also incorporates a flexible variance model, which makes it more capable of approximating surfaces with varying volatility. Compared to the commonly used stationary Gaussian process model, the new predictor is numerically more stable and can more accurately approximate complex surfaces when the experimental design is sparse. In addition, the new model can also improve the prediction intervals by quantifying the change of local variability associated with the response. Advantages of the new predictor are demonstrated using several examples.

Article information

Source
Ann. Appl. Stat., Volume 6, Number 4 (2012), 1838-1860.

Dates
First available in Project Euclid: 27 December 2012

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1356629062

Digital Object Identifier
doi:10.1214/12-AOAS570

Mathematical Reviews number (MathSciNet)
MR3058685

Zentralblatt MATH identifier
1257.62089

Keywords
Computer experiments functional approximation kriging nugget nonstationary Gaussian process

Citation

Ba, Shan; Joseph, V. Roshan. Composite Gaussian process models for emulating expensive functions. Ann. Appl. Stat. 6 (2012), no. 4, 1838--1860. doi:10.1214/12-AOAS570. https://projecteuclid.org/euclid.aoas/1356629062


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