E. V. Burnaev, A. A. Zaytsev, V. G. Spokoiny, “The Bernstein–von Mises theorem for regression based on Gaussian Processes”, Russian Math. Surveys, 68:5 (2013), 954

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Russian Mathematical Surveys, 2013, Volume 68, Issue 5, Pages 954–956
DOI: https://doi.org/10.1070/RM2013v068n05ABEH004863 (Mi rm9546)

This article is cited in 12 scientific papers (total in 12 papers)

In the Moscow Mathematical Society
Communications of the Moscow Mathematical Society

The Bernstein–von Mises theorem for regression based on Gaussian Processes

E. V. Burnaev^abc, A. A. Zaytsev^ac, V. G. Spokoiny^deb

^a Institute for Information Transmission Problems of the Russian Academy of Sciences
^b Moscow Institute of Physics and Technology (PreMoLab)
^c Datadvance
^d Weierstrass Institute
^e Humboldt University, Berlin

English version PDF (358 kB) Citations (12) Russian version article

References:

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DOI: https://doi.org/10.1070/RM2013v068n05ABEH004863

Funding agency	Grant number
Ministry of Education and Science of the Russian Federation	11.G34.31.0073
Russian Foundation for Basic Research	13-01-12447_офи_м2 13-01-00521

Presented: V. M. Buchstaber
Accepted: 17.08.2013

Bibliographic databases:

Document Type: Article

MSC: 62C10, 62G08, 62G05, 62J02, 62F10, 62F15

Language: English

Original paper language: Russian

Citation: E. V. Burnaev, A. A. Zaytsev, V. G. Spokoiny, “The Bernstein–von Mises theorem for regression based on Gaussian Processes”, Russian Math. Surveys, 68:5 (2013), 954–956

Citation in format AMSBIB

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\vol 68

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Linking options:

https://www.mathnet.ru/eng/rm9546

https://doi.org/10.1070/RM2013v068n05ABEH004863

https://www.mathnet.ru/eng/rm/v68/i5/p179

This publication is cited in the following 12 articles:

Burnaev E.V., “Algorithmic Foundations of Predictive Analytics in Industrial Engineering Design”, J. Commun. Technol. Electron., 64:12 (2019), 1485–1492
Evgenii Egorov, Kirill Neklydov, Ruslan Kostoev, Evgeny Burnaev, Lecture Notes in Computer Science, 11554, Advances in Neural Networks – ISNN 2019, 2019, 409
A. Kuleshov, A. Bernstein, E. Burnaev, “Kernel regression on manifold valued data”, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (Dsaa), Proceedings of the International Conference on Data Science and Advanced Analytics, IEEE, 2018, 120–129
Kuleshov A., Bernstein A., Burnaev E., “Manifold Learning Regression With Non-Stationary Kernels”, Artificial Neural Networks in Pattern Recognition, Annpr 2018, Lecture Notes in Artificial Intelligence, 11081, eds. Pancioni L., Schwenker F., Trentin E., Springer International Publishing Ag, 2018, 152–164
A. Zaytsev, E. Burnaev, “Large scale variable fidelity surrogate modeling”, Ann. Math. Artif. Intell., 81:1-2 (2017), 167–186
E. Burnaev, I. Panin, B. Sudret, “Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions”, Ann. Math. Artif. Intell., 81:1-2 (2017), 187–207
E. V. Burnaev, M. E. Panov, A. A. Zaytsev, “Regression on the basis of nonstationary Gaussian processes with Bayesian regularization”, J. Commun. Technol. Electron., 61:6 (2016), 661–671
E. Burnaev, I. Nazarov, “Conformalized kernel ridge regression”, 15Th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, IEEE, 2016, 45–52
M. Belyaev, E. Burnaev, E. Kapushev, M. Panov, P. Prikhodko, D. Vetrov, D. Yarotsky, “GTApprox: Surrogate modeling for industrial design”, Adv. Eng. Softw., 102 (2016), 29–39
A. Zaytsev, Lecture Notes in Computer Science, 9653, Conformal and Probabilistic Prediction with Applications, 2016, 147
Evgeny Burnaev, Ivan Nazarov, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016, 45
A. A. Zaytsev, E. V. Burnaev, V. G. Spokoiny, “Properties of the Bayesian parameter estimation of a regression based on Gaussian processes”, J. Math. Sci., 203:6 (2014), 789–798

Citing articles in Google Scholar: Russian citations, English citations
Related articles in Google Scholar: Russian articles, English articles

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Abstract page:	870
Russian version PDF:	433
English version PDF:	25
References:	76
First page:	72

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