Usage of spectral models of random fields for analysis of numerical integration algorithms

Name: Usage of spectral models of random fields for analysis of numerical integration algorithms
Keywords: Gauss spectral model, random variable, stochastic integral, Monte Carlo methods, Inference from stochastic processes and spectral analysis, Random fields, weak convergence, Monte-Carlo method, numerical integration algorithms

Vojtishek, A. V.; Kablukova, E. G.; Bulgakova, T. E.

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Usage of spectral models of random fields for analysis of numerical integration algorithms

descriptionPublicationkeyboard_double_arrow_right Article Publisher:Russian Academy of Sciences - RAS (Rossiĭskaya Akademiya Nauk - RAN), Siberian Branch (Sibirskoe Otdelenie), Institute of Computational Technologies (Institut Vychislitel'nykh Tekhnologiĭ), Novosibirsk

Authors: Vojtishek, A. V.; Kablukova, E. G.; Bulgakova, T. E.;

Usage of spectral models of random fields for analysis of numerical integration algorithms

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Abstract

Summary: We use the model trajectories of stochastic functions for testing numerical integration algorithms. Such approach allows us to assure the independence of testing, to get necessary properties of integrands (smoothness, `complexity' of calculations, etc.), and to derive the analytic formulas for probabilistic estimates of error. The problem of weak convergence of the considered models is also investigated.

Keywords

Gauss spectral model, random variable, stochastic integral, Monte Carlo methods, Inference from stochastic processes and spectral analysis, Random fields, weak convergence, Monte-Carlo method, numerical integration algorithms

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