title: Markov chain Monte Carlo method in Bayesian reconstruction of dynamical systems from noisy chaotic time series.
abstract: The impossibility to use the MCMC (Markov chain Monte Carlo) methods for long noisy chaotic time series (TS) (due to high computational complexity) is a serious limitation for reconstruction of dynamical systems (DSs). In particular, it does not allow one to use the universal Bayesian approach for reconstruction of a DS in the most interesting case of the unknown evolution operator of the system. We propose a technique that makes it possible to use the MCMC methods for Bayesian reconstruction of a DS from noisy chaotic TS of arbitrary long duration.
authors.affiliation: Institute of Applied Physics, Russian Academy of Sciences, 46, Uljanov Street, Nizhniy Novgorod 603950, Russia. loskutov@appl.sci-nnov.ru
authors.forename: E M
authors.initials: EM
authors.lastname: Loskutov
authors.forename: Ya I
authors.initials: YI
authors.lastname: Molkov
authors.forename: D N
authors.initials: DN
authors.lastname: Mukhin
authors.forename: A M
authors.initials: AM
authors.lastname: Feigin