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Publication

KIAM Preprint № 91, Moscow, 2016
Authors: Kalmetiev R.Sh., Orlov Y. N.
Uncertainty analysis of deterministic models with gaussian process approximation
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
Publication language: russian, pages: 20
Research direction:
Mathematical modelling in actual problems of science and technics
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