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This article is cited in 2 scientific papers (total in 2 papers)
Numerical methods of identification for discrete-state continuous-time Markov processes
L. S. Kuravsky, P. A. Marmalyuk, G. A. Yuryev, P. N. Dumin Moscow State University of Psychology and Education, Computer Science Faculty
Abstract:
A numerical technique for identification of discrete-state continuous-time Markov models is presented. Appropriate algorithms including gradient method, the exhaustive search method and the brute force of significant parameters based on sensitivity analysis of minimization criterion are presented. A study of performance characteristics was carried out, the procedure and conditions of computer experiments are presented including types of models in use, parameters of the algorithms, parameters of the computer system. Analysis of computer experiments showed advantages of the developed identification methods over the classical first order gradient method.
Keywords:
Markov models, models identification, multivariate non-linear optimization.
Received: 05.05.2016
Citation:
L. S. Kuravsky, P. A. Marmalyuk, G. A. Yuryev, P. N. Dumin, “Numerical methods of identification for discrete-state continuous-time Markov processes”, Matem. Mod., 29:5 (2017), 133–146
Linking options:
https://www.mathnet.ru/eng/mm3853 https://www.mathnet.ru/eng/mm/v29/i5/p133
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Abstract page: | 313 | Full-text PDF : | 172 | References: | 40 | First page: | 15 |
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