Startseite Mathematik Approximate spectral models of random processes with periodic properties
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Approximate spectral models of random processes with periodic properties

  • Alisa M. Medvyatskaya und Vasily A. Ogorodnikov EMAIL logo
Veröffentlicht/Copyright: 26. Dezember 2019

Abstract

We consider approaches to simulation of periodically correlated random processes based on the nonstandard spectral representation of the process with parameters periodically varying in time and also on spectral representations using the vector stationary Gaussian processes.

MSC 2010: 68U20; 60K40; 65C05
  1. Funding: This work was partly financially supported by the Russian Foundation for Basic Research (projects No. 18–01–00149-a, 17–07–00775-a), by the Russian Foundation for Basic Research and the Government of the Novosibirsk region according to the research project No. 19–41–543001-r-mol-a.

References

[1] V. N. Bokov, L. I. Lopatukhin, S. M. Mikulinskaya, V. A. Rozhkov, and S. A. Rumyantseva, On the inter-annual variability of waves. In: Problems of Study and Mathematical Modelling of Wind Waves. Gidrometeoizdat, St. Petersburg, 1995, pp. 446– 454 (in Russian).Suche in Google Scholar

[2] Ya. P. Dragan, V. A. Rozhkov, and I. N. Yavorskii, Methods of Probabilistic Analysis of Rhythmics of Oceanological Processes. Gidrometeoizdat, Leningrad, 1987 (in Russian).Suche in Google Scholar

[3] K. V. Derenok and V. A. Ogorodnikov, Numerical simulation of significant long-term decreases in air temperature. Russ. J. Numer. Anal. Math. Modelling23 (2008), No. 3, 223–277.10.1515/RJNAMM.2008.014Suche in Google Scholar

[4] N. A. Kargapolova and V. A. Ogorodnikov, Inhomogeneous Markov chains with periodic matrices of transition probabilities and their application to simulation of meteorological processes. Russ. J. Numer. Anal. Math. Modelling27 (2012), No. 3, 213–228.10.1515/rnam-2012-0012Suche in Google Scholar

[5] D. I. Kazakevich, Basic Concepts of the Random Functions Theorey in Hydrometeorological Problems. Gidrometeoizdat, Leningrad, 1999 (in Russian).Suche in Google Scholar

[6] G. A. Mikhailov and A. V. Voitishek, Numerical Statistical Modelling. Monte Carlo Methods. Academia Publ. Moscow, 2006 (in Russian).Suche in Google Scholar

[7] V. A. Ogorodnikov and S. M. Prigarin, Numerical Modeling of Random Processes and Fields: Algorithm and Applications. VSP, Utrecht, The Netherlands, 1996.10.1515/9783110941999Suche in Google Scholar

[8] V. A. Ogorodnikov, O. V. Sereseva, and N. A. Kargapolova, Stochastic models of piecewise-constant and piecewise-linear non-Gaussian processes based on Poisson flow. Russ. J. Numer. Anal. Math. Modelling31 (2016), No. 3, 179–185.10.1515/rnam-2016-0018Suche in Google Scholar

[9] Yu. I. Palagin, S. V. Fedotov, and A. S. Shalygin, Parametric models for statistical modelling of vector inhomogeneous random fields. Autom. Remote Control51 (1990), No. 6, 789–797.Suche in Google Scholar

[10] S. M. Prigarin, Methods of Numerical Modelling of Random Processes and Fields. INM&MG SB RAS, Novosibirsk, 2005 (in Russian).Suche in Google Scholar

[11] V. A. Rozhkov, Methods of Probabilistic Analysis of Oceanological Processes. Gidrometeoizdat, Leningrad, 1979 (in Russian).Suche in Google Scholar

[12] V. A. Rozhkov and Yu. A. Trapeznikov, Probabilistic Models of Oceanological Processes. Gidrometeoizdat, Leningrad, 1990 (in Russian).Suche in Google Scholar

Received: 2019-07-01
Accepted: 2019-10-22
Published Online: 2019-12-26
Published in Print: 2019-12-18

© 2019 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 15.1.2026 von https://www.degruyterbrill.com/document/doi/10.1515/rnam-2019-0030/pdf
Button zum nach oben scrollen