Bayesian reconstruction of chaotic dynamical systems

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Renate Meyer, Nelson Christensen

Abstract

We present a Bayesian approach to the problem of determining parameters of nonlinear models from time series of noisy data. Recent approaches to this problem have been statistically flawed. By applying a Markov chain Monte Carlo algorithm, specifically the Gibbs sampler, we estimate the parameters of chaotic maps. A complete statistical analysis is presented, the Gibbs sampler method is described in detail, and example applications are presented.

References

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Citations

May 3, 2003·Methods : a Companion to Methods in Enzymology·Rong-Fu Wang
Apr 8, 2006·Chaos·M Eugenia Mera, Manuel Morán
Oct 4, 2005·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·V N SmelyanskiyA Bandrivskyy
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Jan 22, 2002·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Renate Meyer, Nelson Christensen
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Mar 15, 2003·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Kevin Judd

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