Gaussian Process Models for Computer Experiments With Qualitative and Quantitative Factors

Technometrics ◽  
2008 ◽  
Vol 50 (3) ◽  
pp. 383-396 ◽  
Author(s):  
Peter Z. G Qian ◽  
Huaiqing Wu ◽  
C. F. Jeff Wu
2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Weiyan Mu ◽  
Qiuyue Wei ◽  
Dongli Cui ◽  
Shifeng Xiong

Recently it becomes a growing trend to study complex systems which contain multiple computer codes with different levels of accuracy, and a number of hierarchical Gaussian process models are proposed to handle such multiple-fidelity codes. This paper derives the best linear unbiased prediction for three popular classes of multiple-level Gaussian process models. The predictors all have explicit expressions at each untried point. Empirical best linear unbiased predictors are also provided by plug-in methods with generalized maximum likelihood estimators of unknown parameters.


Technometrics ◽  
2006 ◽  
Vol 48 (4) ◽  
pp. 478-490 ◽  
Author(s):  
Crystal Linkletter ◽  
Derek Bingham ◽  
Nicholas Hengartner ◽  
David Higdon ◽  
Kenny Q Ye

2014 ◽  
Vol 134 (11) ◽  
pp. 1708-1715
Author(s):  
Tomohiro Hachino ◽  
Kazuhiro Matsushita ◽  
Hitoshi Takata ◽  
Seiji Fukushima ◽  
Yasutaka Igarashi

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