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

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Applications of dynamical inference to the analysis of noisy biological time series with hidden dynamical variables. / Duggento, A.; Luchinsky, D. G.; Smelyanskiy, V. N. et al.
Noise and fluctuations : 20th International Conference on Noise and Fluctuations (ICNF-2009). ed. / M. Macucci; G. Basso. Vol. 1129 Melville, N. Y.: American Institute of Physics, 2009. p. 531-534 (AIP Conference Proceedings).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Duggento, A, Luchinsky, DG, Smelyanskiy, VN, Millonas, M & McClintock, PVE 2009, Applications of dynamical inference to the analysis of noisy biological time series with hidden dynamical variables. in M Macucci & G Basso (eds), Noise and fluctuations : 20th International Conference on Noise and Fluctuations (ICNF-2009). vol. 1129, AIP Conference Proceedings, American Institute of Physics, Melville, N. Y., pp. 531-534. https://doi.org/10.1063/1.3140527

APA

Duggento, A., Luchinsky, D. G., Smelyanskiy, V. N., Millonas, M., & McClintock, P. V. E. (2009). Applications of dynamical inference to the analysis of noisy biological time series with hidden dynamical variables. In M. Macucci, & G. Basso (Eds.), Noise and fluctuations : 20th International Conference on Noise and Fluctuations (ICNF-2009) (Vol. 1129, pp. 531-534). (AIP Conference Proceedings). American Institute of Physics. https://doi.org/10.1063/1.3140527

Vancouver

Duggento A, Luchinsky DG, Smelyanskiy VN, Millonas M, McClintock PVE. Applications of dynamical inference to the analysis of noisy biological time series with hidden dynamical variables. In Macucci M, Basso G, editors, Noise and fluctuations : 20th International Conference on Noise and Fluctuations (ICNF-2009). Vol. 1129. Melville, N. Y.: American Institute of Physics. 2009. p. 531-534. (AIP Conference Proceedings). doi: 10.1063/1.3140527

Author

Duggento, A. ; Luchinsky, D. G. ; Smelyanskiy, V. N. et al. / Applications of dynamical inference to the analysis of noisy biological time series with hidden dynamical variables. Noise and fluctuations : 20th International Conference on Noise and Fluctuations (ICNF-2009). editor / M. Macucci ; G. Basso. Vol. 1129 Melville, N. Y. : American Institute of Physics, 2009. pp. 531-534 (AIP Conference Proceedings).

Bibtex

@inbook{15069553ddb54a38b9ffb96c19715a39,
title = "Applications of dynamical inference to the analysis of noisy biological time series with hidden dynamical variables.",
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).",
author = "A. Duggento and Luchinsky, {D. G.} and Smelyanskiy, {V. N.} and M. Millonas and McClintock, {P. V. E.}",
year = "2009",
month = apr,
day = "23",
doi = "10.1063/1.3140527",
language = "English",
volume = "1129",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics",
pages = "531--534",
editor = "M. Macucci and G. Basso",
booktitle = "Noise and fluctuations : 20th International Conference on Noise and Fluctuations (ICNF-2009)",

}

RIS

TY - CHAP

T1 - Applications of dynamical inference to the analysis of noisy biological time series with hidden dynamical variables.

AU - Duggento, A.

AU - Luchinsky, D. G.

AU - Smelyanskiy, V. N.

AU - Millonas, M.

AU - McClintock, P. V. E.

PY - 2009/4/23

Y1 - 2009/4/23

N2 - 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).

AB - 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).

U2 - 10.1063/1.3140527

DO - 10.1063/1.3140527

M3 - Chapter

VL - 1129

T3 - AIP Conference Proceedings

SP - 531

EP - 534

BT - Noise and fluctuations : 20th International Conference on Noise and Fluctuations (ICNF-2009)

A2 - Macucci, M.

A2 - Basso, G.

PB - American Institute of Physics

CY - Melville, N. Y.

ER -