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Selection of a covariance kernel for a Gaussian random field aimed for modeling global optimization problems. / Žilinskas, Antanas; Zhigljavsky, Anatoly; Nekrutkin, Vladimir; Kornikov, Vladimir.

14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP (LEGO). ed. / MTM Emmerich; AH Deutz; SC Hille; YD Sergeyev. Vol. 2070 American Institute of Physics, 2019. 20043 (AIP Conference Proceedings; Vol. 2070).

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Harvard

Žilinskas, A, Zhigljavsky, A, Nekrutkin, V & Kornikov, V 2019, Selection of a covariance kernel for a Gaussian random field aimed for modeling global optimization problems. in MTM Emmerich, AH Deutz, SC Hille & YD Sergeyev (eds), 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP (LEGO). vol. 2070, 20043, AIP Conference Proceedings, vol. 2070, American Institute of Physics, 14th International Global Optimization Workshop, LeGO 2018, Leiden, Netherlands, 18/09/18. https://doi.org/10.1063/1.5090010

APA

Žilinskas, A., Zhigljavsky, A., Nekrutkin, V., & Kornikov, V. (2019). Selection of a covariance kernel for a Gaussian random field aimed for modeling global optimization problems. In MTM. Emmerich, AH. Deutz, SC. Hille, & YD. Sergeyev (Eds.), 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP (LEGO) (Vol. 2070). [20043] (AIP Conference Proceedings; Vol. 2070). American Institute of Physics. https://doi.org/10.1063/1.5090010

Vancouver

Žilinskas A, Zhigljavsky A, Nekrutkin V, Kornikov V. Selection of a covariance kernel for a Gaussian random field aimed for modeling global optimization problems. In Emmerich MTM, Deutz AH, Hille SC, Sergeyev YD, editors, 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP (LEGO). Vol. 2070. American Institute of Physics. 2019. 20043. (AIP Conference Proceedings). https://doi.org/10.1063/1.5090010

Author

Žilinskas, Antanas ; Zhigljavsky, Anatoly ; Nekrutkin, Vladimir ; Kornikov, Vladimir. / Selection of a covariance kernel for a Gaussian random field aimed for modeling global optimization problems. 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP (LEGO). editor / MTM Emmerich ; AH Deutz ; SC Hille ; YD Sergeyev. Vol. 2070 American Institute of Physics, 2019. (AIP Conference Proceedings).

BibTeX

@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",
isbn = "9780735417984",
volume = "2070",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics",
editor = "MTM Emmerich and AH Deutz and SC Hille and YD Sergeyev",
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",

}

RIS

TY - GEN

T1 - Selection of a covariance kernel for a Gaussian random field aimed for modeling global optimization problems

AU - Žilinskas, Antanas

AU - Zhigljavsky, Anatoly

AU - Nekrutkin, Vladimir

AU - Kornikov, Vladimir

PY - 2019/2/12

Y1 - 2019/2/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85061906735&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/selection-covariance-kernel-gaussian-random-field-aimed-modeling-global-optimization-problems

U2 - 10.1063/1.5090010

DO - 10.1063/1.5090010

M3 - Conference contribution

AN - SCOPUS:85061906735

SN - 9780735417984

VL - 2070

T3 - AIP Conference Proceedings

BT - 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP (LEGO)

A2 - Emmerich, MTM

A2 - Deutz, AH

A2 - Hille, SC

A2 - Sergeyev, YD

PB - American Institute of Physics

T2 - 14th International Global Optimization Workshop, LeGO 2018

Y2 - 18 September 2018 through 21 September 2018

ER -

ID: 39305355