Entropy-probabilistic modeling of Gaussian stochastic systems
Matematičeskoe modelirovanie, Tome 24 (2012) no. 1, pp. 88-102.

Voir la notice de l'article provenant de la source Math-Net.Ru

There is the new entropy-probabilistic approach of modeling of stochastic systems. It is based on the representation of system in the form of a many-dimensional normal stochastic vector. The entropy- probabilistic model is described and investigated.
Keywords: normal stochastic vector, system, model, entropy, random variable, variance
Mots-clés : covariance matrix.
@article{MM_2012_24_1_a5,
     author = {A. N. Tyrsin and I. S. Sokolova},
     title = {Entropy-probabilistic modeling of {Gaussian} stochastic systems},
     journal = {Matemati\v{c}eskoe modelirovanie},
     pages = {88--102},
     publisher = {mathdoc},
     volume = {24},
     number = {1},
     year = {2012},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MM_2012_24_1_a5/}
}
TY  - JOUR
AU  - A. N. Tyrsin
AU  - I. S. Sokolova
TI  - Entropy-probabilistic modeling of Gaussian stochastic systems
JO  - Matematičeskoe modelirovanie
PY  - 2012
SP  - 88
EP  - 102
VL  - 24
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MM_2012_24_1_a5/
LA  - ru
ID  - MM_2012_24_1_a5
ER  - 
%0 Journal Article
%A A. N. Tyrsin
%A I. S. Sokolova
%T Entropy-probabilistic modeling of Gaussian stochastic systems
%J Matematičeskoe modelirovanie
%D 2012
%P 88-102
%V 24
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MM_2012_24_1_a5/
%G ru
%F MM_2012_24_1_a5
A. N. Tyrsin; I. S. Sokolova. Entropy-probabilistic modeling of Gaussian stochastic systems. Matematičeskoe modelirovanie, Tome 24 (2012) no. 1, pp. 88-102. http://geodesic.mathdoc.fr/item/MM_2012_24_1_a5/

[1] Voronin A.A., Mishin S.P., Optimalnye ierarkhicheskie struktury, IPU RAN, M., 2003, 214 pp.

[2] Prangishvili I.V., Entropiinye i drugie sistemnye zakonomernosti: Voprosy upravleniya slozhnymi sistemami, Nauka, M., 2003, 428 pp. | Zbl

[3] Vilson A.Dzh., Entropiinye metody modelirovaniya slozhnykh sistem, Nauka, M., 1978, 226 pp. | MR

[4] Popkov Yu.S., Teoriya makrosistem (ravnovesnye modeli), Editorial URSS, M., 1999, 320 pp. | MR

[5] Fedulov A.G., Fedulov Yu.G., Tsygichko V.N., Vvedenie v teoriyu statisticheski nenadezhnykh reshenii, 2-e izd., KomKniga, M., 2007, 280 pp. | MR

[6] Skorobogatov S.M., Katastrofy i zhivuchest zhelezobetonnykh sooruzhenii (klassifikatsiya i elementy teorii), UrGUPS, Ekaterinburg, 2009, 512 pp.

[7] Shternberg M.I., “Sinergetika i biologiya”, Voprosy filosofii, 1999, no. 2, 95–108

[8] Shennon K., Raboty po teorii informatsii i kibernetike, Izdatelstvo inostrannoi literatury, M., 1963, 830 pp.

[9] Tyrsin A.N., Klyavin I.A., “Povyshenie tochnosti otsenki entropii sluchainykh eksperimentalnykh dannykh”, Sistemy upravleniya i informatsionnye tekhnologii, 2010, no. 1(39), 87–90

[10] Shredinger E., Chto takoe zhizn? S tochki zreniya fizika, 2-e izd., Atomizdat, M., 1972, 88 pp.

[11] Simon M.K., Probability Distributions Involving Gaussian Random Variables, Springer, 2002, 218 pp.

[12] Teoriya sistem i sistemnyi analiz v upravlenii organizatsiyami, Spravochnik, eds. Volkova V.N., Emelyanov A.A., Finansy i statistika, M., 2006, 848 pp.

[13] Fikhtengolts G.M., Kurs differentsialnogo i integralnogo ischisleniya, V 3-kh tomakh, v. 1, 7-e izd., Nauka, M., 1969, 608 pp.