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Inference of a nonlinear stochastic model of the cardiorespiratory interaction.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Inference of a nonlinear stochastic model of the cardiorespiratory interaction. / Stefanovska, Aneta; Luchinsky, D. G.; McClintock, Peter V. E. et al.
In: Physical review letters, Vol. 94, No. 9, 08.03.2005, p. 098101.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Stefanovska, A, Luchinsky, DG, McClintock, PVE & Smelyanskiy, VN 2005, 'Inference of a nonlinear stochastic model of the cardiorespiratory interaction.', Physical review letters, vol. 94, no. 9, pp. 098101. https://doi.org/10.1103/PhysRevLett.94.098101

APA

Stefanovska, A., Luchinsky, D. G., McClintock, P. V. E., & Smelyanskiy, V. N. (2005). Inference of a nonlinear stochastic model of the cardiorespiratory interaction. Physical review letters, 94(9), 098101. https://doi.org/10.1103/PhysRevLett.94.098101

Vancouver

Stefanovska A, Luchinsky DG, McClintock PVE, Smelyanskiy VN. Inference of a nonlinear stochastic model of the cardiorespiratory interaction. Physical review letters. 2005 Mar 8;94(9):098101. doi: 10.1103/PhysRevLett.94.098101

Author

Stefanovska, Aneta ; Luchinsky, D. G. ; McClintock, Peter V. E. et al. / Inference of a nonlinear stochastic model of the cardiorespiratory interaction. In: Physical review letters. 2005 ; Vol. 94, No. 9. pp. 098101.

Bibtex

@article{c82813f553ea4d91898b573156e84cbe,
title = "Inference of a nonlinear stochastic model of the cardiorespiratory interaction.",
abstract = "We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set of polynomial basis functions representing the nonlinear force governing system oscillations. The strength and direction of coupling and noise intensity are simultaneously inferred from a univariate blood pressure signal. Our new inference technique does not require extensive global optimization, and it is applicable to a wide range of complex dynamical systems subject to noise.",
author = "Aneta Stefanovska and Luchinsky, {D. G.} and McClintock, {Peter V. E.} and Smelyanskiy, {V. N.}",
note = "First use of Bayesian inference to build a nonlinear stochastic model of the cardio-respiratory interaction in terms of polynomial basis functions, directly from a univariate blood pressure signal. The technique is widely applicable in dynamical modelling. RAE_import_type : Journal article RAE_uoa_type : Physics",
year = "2005",
month = mar,
day = "8",
doi = "10.1103/PhysRevLett.94.098101",
language = "English",
volume = "94",
pages = "098101",
journal = "Physical review letters",
publisher = "American Physical Society",
number = "9",

}

RIS

TY - JOUR

T1 - Inference of a nonlinear stochastic model of the cardiorespiratory interaction.

AU - Stefanovska, Aneta

AU - Luchinsky, D. G.

AU - McClintock, Peter V. E.

AU - Smelyanskiy, V. N.

N1 - First use of Bayesian inference to build a nonlinear stochastic model of the cardio-respiratory interaction in terms of polynomial basis functions, directly from a univariate blood pressure signal. The technique is widely applicable in dynamical modelling. RAE_import_type : Journal article RAE_uoa_type : Physics

PY - 2005/3/8

Y1 - 2005/3/8

N2 - We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set of polynomial basis functions representing the nonlinear force governing system oscillations. The strength and direction of coupling and noise intensity are simultaneously inferred from a univariate blood pressure signal. Our new inference technique does not require extensive global optimization, and it is applicable to a wide range of complex dynamical systems subject to noise.

AB - We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set of polynomial basis functions representing the nonlinear force governing system oscillations. The strength and direction of coupling and noise intensity are simultaneously inferred from a univariate blood pressure signal. Our new inference technique does not require extensive global optimization, and it is applicable to a wide range of complex dynamical systems subject to noise.

U2 - 10.1103/PhysRevLett.94.098101

DO - 10.1103/PhysRevLett.94.098101

M3 - Journal article

VL - 94

SP - 098101

JO - Physical review letters

JF - Physical review letters

IS - 9

ER -