Applications of dynamical inference to the analysis of noisy biological time series with hidden dynamical variables.

Duggento, A. and Luchinsky, D. G. and Smelyanskiy, V. N. and Millonas, M. and McClintock, P. V. E. (2009) Applications of dynamical inference to the analysis of noisy biological time series with hidden dynamical variables. In: Noise and fluctuations : 20th International Conference on Noise and Fluctuations (ICNF-2009). AIP Conference Proceedings, 1129 . American Institute of Physics, Melville, N. Y., pp. 531-534.

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Abstract

We present a Bayesian framework for parameter inference in noisy, non-stationary, nonlinear, dynamical systems. The technique is implemented in two distinct ways: (i) Lightweight implementation: to be used for on-line analysis, allowing multiple parameter estimation, optimal compensation for dynamical noise, and reconstruction by integration of the hidden dynamical variables, but with some limitations on how the noise appears in the dynamics; (ii) Full scale implementation: of the technique with extensive numerical simulations (MCMC), allowing for more sophisticated reconstruction of hidden dynamical trajectories and dealing better with sources of noise external to the dynamics (measurements noise).

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: /dk/atira/pure/researchoutput/libraryofcongress/qc
Subjects:
Departments: Faculty of Science and Technology > Physics
ID Code: 31244
Deposited By: Professor P. V. E. McClintock
Deposited On: 06 Jan 2010 14:41
Refereed?: No
Published?: Published
Last Modified: 15 Sep 2019 23:51
URI: https://eprints.lancs.ac.uk/id/eprint/31244

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