@inproceedings{51fda94d9a4e48f5bd55427bd23cef52,
title = "Selection of a covariance kernel for a Gaussian random field aimed for modeling global optimization problems",
abstract = "Bayesian approach is actively used to develop global optimization algorithms aimed at expensive black box functions. One of the challenges in this approach is the selection of an appropriate model for the objective function. Normally, a Gaussian random field is chosen as a theoretical model. However, the problem of estimation of parameters, using objective function values, is not thoroughly researched. In this paper, we consider the behavior of maximum likelihood estimators (MLEs) of parameters of the homogeneous isotropic Gaussian random field with squared exponential covariance function. We also compare properties of exponential covariance function models.",
author = "Antanas {\v Z}ilinskas and Anatoly Zhigljavsky and Vladimir Nekrutkin and Vladimir Kornikov",
year = "2019",
month = feb,
day = "12",
doi = "10.1063/1.5090010",
language = "English",
volume = "2070",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics",
editor = "Deutz, {Andre H.} and Hille, {Sander C.} and Sergeyev, {Yaroslav D.} and Emmerich, {Michael T. M.}",
booktitle = "14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP (LEGO)",
address = "United States",
note = "14th International Global Optimization Workshop, LeGO 2018 ; Conference date: 18-09-2018 Through 21-09-2018",
}