Voir la notice de l'article provenant de la source Math-Net.Ru
[1] Averina T. A., Rybakov K. A., “Using maximum cross section method for filtering jump-diffusion random processes”, Russ. J. of Numerical Analysis and Mathematical Modelling, 35:2 (2020), 55–67 | DOI | MR | Zbl
[2] Paraev Yu. I., Vvedenie v statisticheskuyu dinamiku protsessov upravleniya i filtratsii, Sov. radio, M., 1976
[3] Mikhailov G. A., Voitishek A. V., Chislennoe statisticheskoe modelirovanie. Metody Monte-Karlo, Akademiya, M., 2006
[4] Mikhailov G. A., “K voprosu o postroenii ekonomichnykh algoritmov modelirovaniya sluchainykh velichin”, Zhurn. vychisl. matem. i mat. fiziki, 6:6 (1966), 1134–1136 | Zbl
[5] Averina T. A., “Novye algoritmy statisticheskogo modelirovaniya neodnorodnykh puassonovskikh ansamblei”, Zhurn. vychisl. matem. i mat fiziki, 50:1 (2010), 16–23 | MR | Zbl
[6] Sobol I. M., Chislennye metody Monte-Karlo, Nauka, M., 1973
[7] Mikhailov G. A., Marchenko M. A., “Parallel realization of statistical simulation and random number generators”, Russ. J. of Numerical Analysis and Mathematical Modelling, 17:1 (2002), 113–124 | MR | Zbl
[8] Averina T. A., Rybakov K. A., “Priblizhennoe reshenie zadachi prognozirovaniya dlya stokhasticheskikh sistem diffuzionno-skachkoobraznogo tipa”, Sib. zhurn. vychisl. matematiki, 20:1 (2017), 1–13 | Zbl
[9] Ermakov S. M., Mikhailov G. A., Kurs statisticheskogo modelirovaniya, Nauka, M., 1976 | MR
[10] Prigarin S. M., Metody chislennogo modelirovaniya sluchainykh protsessov i polei, Izd-vo IVMiMG SO RAN, Novosibirsk, 2005