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Keldysh Institute preprints, 2016, 091, 20 pp. (Mi ipmp2165)  

Uncertainty analysis of deterministic models with Gaussian process approximation

R. S. Kalmetev, Yu. N. Orlov


Abstract: Approach to solve the problems of uncertainty analysis of deterministic models based on Gaussian random fields is introduced. To construct the regressions of different models covariance functions with some common hyperparameters are used. We consider the practical examples of data on nuclear reactions, as well as the problem of non-stationary time series clustering.

Keywords: uncertainties analysis, deterministic models, stochastic approximation ratio, Gaussian processes, non-stationary time series.

Funding Agency Grant Number
Russian Foundation for Basic Research 15-08-02575_а


DOI: https://doi.org/10.20948/prepr-2016-91

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Citation: R. S. Kalmetev, Yu. N. Orlov, “Uncertainty analysis of deterministic models with Gaussian process approximation”, Keldysh Institute preprints, 2016, 091, 20 pp.

Citation in format AMSBIB
\Bibitem{KalOrl16}
\by R.~S.~Kalmetev, Yu.~N.~Orlov
\paper Uncertainty analysis of deterministic models with Gaussian process approximation
\jour Keldysh Institute preprints
\yr 2016
\papernumber 091
\totalpages 20
\mathnet{http://mi.mathnet.ru/ipmp2165}
\crossref{https://doi.org/10.20948/prepr-2016-91}


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