Statistical Methods of Analysis and Filtering for Continuous Stochastic Systems
Author(s):
Konstantin A. Rybakov
Moscow aviation institute (national research university),
Mathematical cybernetics department, associate professor
Candidate of physico-mathematical sciences
rkoffice@mail.ru
Abstract:
The analysis and filtering problems for continuous-time stochastic
systems (mathematical models are described by stochastic differential equations)
is considered in the new book “Statistical methods of analysis and filtering
for continuous stochastic systems”. The main method for the analysis
and filtering problems is the statistical modeling method (Monte Carlo method).
This book is intended for engineers and scientists, as well as for
students and graduate students that specialize in the statistical modeling
and problems of continuous-time stochastic dynamical systems,
analysis and filtering for such systems.
Keywords
- analysis
- dynamical systems
- Monte Carlo method
- optimal filtering
- particle filter
- particle method
- statistical modeling
References:
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- Averina T. A., Rybakov K. A. Statisticheskie algoritmy prognozirovaniya dlya nelineinykh stokhasticheskikh sistem diffuzionno-skachkoobraznogo tipa [Statistical prediction algorithms for nonlinear stochastic jump-diffusion systems]. Differential Equations and Control Processes, 2017, no. 2, pp. 130-152. (In Russ. )
