On the maximum likelihood training of gradient-enhanced spatial Gaussian processes. (English) Zbl 1283.62201

Zimmermann, R.

SIAM J. Sci. Comput. 35, No. 6, A2554-A2574 (2013).

MSC:

62M30	Inference from spatial processes
62P30	Applications of statistics in engineering and industry; control charts
65C60	Computational problems in statistics (MSC2010)
62M20	Inference from stochastic processes and prediction
65C20	Probabilistic models, generic numerical methods in probability and statistics

Keywords:

design and analysis of computer experiments; gradient-enhanced Kriging; spatial linear models; response surface; surrogate model; hyper-parameter training; maximum likelihood

Software:

DACE; Matlab; TAU