Gaussian Processes and Polynomial Chaos Expansion for Regression Problem: Linkage via the RKHS and Comparison via the KL Divergence

Gaussian Processes and Polynomial Chaos Expansion for Regression Problem: Linkage via the RKHS and Comparison via the KL Divergence

In this paper, we examine two widely-used approaches, the polynomial chaos expansion (PCE) and Gaussian process (GP) regression, for the development of surrogate models. The theoretical differences between the PCE and GP approximations are discussed. A state-of-the-art PCE approach is constructed ba...

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Bibliographic Details
Main Authors: Liang Yan, Xiaojun Duan, Bowen Liu, Jin Xu
Format: Article
Language:English
Published: MDPI AG 2018-03-01
Series:Entropy
Subjects:
Gaussian process
polynomial chaos expansion
reproducing kernel Hilbert space
Kullback–Leibler divergence
experimental design
Online Access:http://www.mdpi.com/1099-4300/20/3/191

Internet

http://www.mdpi.com/1099-4300/20/3/191

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