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Uspekhi Mat. Nauk, 2013, Volume 68, Issue 5(413), Pages 179–180 (Mi umn9546)  

This article is cited in 6 scientific papers (total in 6 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. Burnaevabc, A. A. Zaytsevac, V. G. Spokoinydeb

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

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


DOI: https://doi.org/10.4213/rm9546

Full text: PDF file (394 kB)
References: PDF file   HTML file

English version:
Russian Mathematical Surveys, 2013, 68:5, 954–956

Bibliographic databases:

Document Type: Article
MSC: 62C10, 62G08, 62G05, 62J02, 62F10, 62F15
Presented: V. M. Buchstaber
Accepted: 17.08.2013

Citation: E. V. Burnaev, A. A. Zaytsev, V. G. Spokoiny, “The Bernstein–von Mises theorem for regression based on Gaussian Processes”, Uspekhi Mat. Nauk, 68:5(413) (2013), 179–180; Russian Math. Surveys, 68:5 (2013), 954–956

Citation in format AMSBIB
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    Citing articles on Google Scholar: Russian citations, English citations
    Related articles on Google Scholar: Russian articles, English articles

    This publication is cited in the following articles:
    1. 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  mathnet  crossref  mathscinet
    2. 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  crossref  isi  elib  scopus
    3. E. Burnaev, I. Nazarov, “Conformalized kernel ridge regression”, 15Th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, IEEE, 2016, 45–52  crossref  isi  scopus
    4. 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  crossref  isi  elib  scopus
    5. A. Zaytsev, E. Burnaev, “Large scale variable fidelity surrogate modeling”, Ann. Math. Artif. Intell., 81:1-2 (2017), 167–186  crossref  mathscinet  zmath  isi  scopus
    6. 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  crossref  mathscinet  zmath  isi  scopus
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