Recovering ‘lost’ information in the presence of noise: application to rodent–predator dynamics.

A Hamiltonian approach is introduced for the reconstruction of trajectories and models of complex stochastic dynamics from noisy measurements. The method converges even when entire trajectory components are unobservable and the parameters are unknown. It is applied to reconstruct nonlinear models of...

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Bibliographic Details
Published in:New Journal of Physics
Main Authors: Smelyanskiy, V. N., Luchinsky, D. G., Millonas, M. M., McClintock, P. V. E.
Format: Article in Journal/Newspaper
Language:English
Published: 2009
Subjects:
Arctic
Tundra
Lapland
Online Access:https://eprints.lancs.ac.uk/id/eprint/31239/
https://eprints.lancs.ac.uk/id/eprint/31239/1/NJP2009LostInfo.pdf
https://doi.org/10.1088/1367-2630/11/5/053012
Description
Summary:A Hamiltonian approach is introduced for the reconstruction of trajectories and models of complex stochastic dynamics from noisy measurements. The method converges even when entire trajectory components are unobservable and the parameters are unknown. It is applied to reconstruct nonlinear models of rodent–predator oscillations in Finnish Lapland and high-Arctic tundra. The projected character of noisy incomplete measurements is revealed and shown to result in a degeneracy of the likelihood function within certain null-spaces. The performance of the method is compared with that of the conventional Markov chain Monte Carlo (MCMC) technique.

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