Markov chain Monte Carlo method in Bayesian reconstruction of dynamical systems from noisy chaotic time series

Details Markov chain Monte Carlo method in Bayesian reconstruction of dynamical systems from noisy chaotic time series Markov chain Monte Carlo method in Bayesian reconstruction of dynamical systems from noisy chaotic time series

Jun 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008phrve..77f6214l&link_type=abstract

Physical Review E, vol. 77, Issue 6, id. 066214

Statistics

Computation

2

Nonlinear Dynamics And Chaos, Computational Methods In Statistical Physics And Nonlinear Dynamics, Time Series Analysis, Time Variability

Scientific paper

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.

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