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Inferential framework for non-stationary dynamics: theory and applications

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Inferential framework for non-stationary dynamics: theory and applications. / Duggento, A.; Luchinsky, D. G.; Smelyanskiy, V. N. et al.
In: Journal of Statistical Mechanics: Theory and Experiment, Vol. 2009, No. 1, P01025, 2009.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Duggento, A, Luchinsky, DG, Smelyanskiy, VN & McCkintock, PVE 2009, 'Inferential framework for non-stationary dynamics: theory and applications', Journal of Statistical Mechanics: Theory and Experiment, vol. 2009, no. 1, P01025. https://doi.org/10.1088/1742-5468/2009/01/P01025

APA

Duggento, A., Luchinsky, D. G., Smelyanskiy, V. N., & McCkintock, P. V. E. (2009). Inferential framework for non-stationary dynamics: theory and applications. Journal of Statistical Mechanics: Theory and Experiment, 2009(1), Article P01025. https://doi.org/10.1088/1742-5468/2009/01/P01025

Vancouver

Duggento A, Luchinsky DG, Smelyanskiy VN, McCkintock PVE. Inferential framework for non-stationary dynamics: theory and applications. Journal of Statistical Mechanics: Theory and Experiment. 2009;2009(1):P01025. doi: 10.1088/1742-5468/2009/01/P01025

Author

Duggento, A. ; Luchinsky, D. G. ; Smelyanskiy, V. N. et al. / Inferential framework for non-stationary dynamics : theory and applications. In: Journal of Statistical Mechanics: Theory and Experiment. 2009 ; Vol. 2009, No. 1.

Bibtex

@article{ffaaf6b31a09450a9d6754b47c437c54,
title = "Inferential framework for non-stationary dynamics: theory and applications",
abstract = "An extended Bayesian inference framework is presented, aiming to infer time-varying parameters in non-stationary nonlinear stochastic dynamical systems. The convergence of the method is discussed. The performance of the technique is studied using, as an example, signal reconstruction for a system of neurons modeled by FitzHugh–Nagumo oscillators: it is applied to reconstruction of the model parameters and elements of the measurement matrix, as well as to inference of the time-varying parameters of the non stationary system. It is shown that the proposed approach is able to reconstruct unmeasured (hidden) variables of the system, to determine the model parameters, to detect stepwise changes of control parameters for each oscillator and to track the continuous evolution of the control parameters in the adiabatic limit.",
keywords = "dynamics (theory), dynamics (experiment), robust and stochastic optimization",
author = "A. Duggento and Luchinsky, {D. G.} and Smelyanskiy, {V. N.} and McCkintock, {P. V. E.}",
year = "2009",
doi = "10.1088/1742-5468/2009/01/P01025",
language = "English",
volume = "2009",
journal = "Journal of Statistical Mechanics: Theory and Experiment",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Inferential framework for non-stationary dynamics

T2 - theory and applications

AU - Duggento, A.

AU - Luchinsky, D. G.

AU - Smelyanskiy, V. N.

AU - McCkintock, P. V. E.

PY - 2009

Y1 - 2009

N2 - An extended Bayesian inference framework is presented, aiming to infer time-varying parameters in non-stationary nonlinear stochastic dynamical systems. The convergence of the method is discussed. The performance of the technique is studied using, as an example, signal reconstruction for a system of neurons modeled by FitzHugh–Nagumo oscillators: it is applied to reconstruction of the model parameters and elements of the measurement matrix, as well as to inference of the time-varying parameters of the non stationary system. It is shown that the proposed approach is able to reconstruct unmeasured (hidden) variables of the system, to determine the model parameters, to detect stepwise changes of control parameters for each oscillator and to track the continuous evolution of the control parameters in the adiabatic limit.

AB - An extended Bayesian inference framework is presented, aiming to infer time-varying parameters in non-stationary nonlinear stochastic dynamical systems. The convergence of the method is discussed. The performance of the technique is studied using, as an example, signal reconstruction for a system of neurons modeled by FitzHugh–Nagumo oscillators: it is applied to reconstruction of the model parameters and elements of the measurement matrix, as well as to inference of the time-varying parameters of the non stationary system. It is shown that the proposed approach is able to reconstruct unmeasured (hidden) variables of the system, to determine the model parameters, to detect stepwise changes of control parameters for each oscillator and to track the continuous evolution of the control parameters in the adiabatic limit.

KW - dynamics (theory)

KW - dynamics (experiment)

KW - robust and stochastic optimization

U2 - 10.1088/1742-5468/2009/01/P01025

DO - 10.1088/1742-5468/2009/01/P01025

M3 - Journal article

VL - 2009

JO - Journal of Statistical Mechanics: Theory and Experiment

JF - Journal of Statistical Mechanics: Theory and Experiment

IS - 1

M1 - P01025

ER -