Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model

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Standard

Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model. / Jensen, Anders Christian; Ditlevsen, Susanne; Kessler , Mathieu; Papaspiliopoulos , Omiros.

I: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, Bind 86, Nr. 4, 2012, s. 041114.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Jensen, AC, Ditlevsen, S, Kessler , M & Papaspiliopoulos , O 2012, 'Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model', Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, bind 86, nr. 4, s. 041114. https://doi.org/10.1103/PhysRevE.86.041114

APA

Jensen, A. C., Ditlevsen, S., Kessler , M., & Papaspiliopoulos , O. (2012). Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model. Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, 86(4), 041114. https://doi.org/10.1103/PhysRevE.86.041114

Vancouver

Jensen AC, Ditlevsen S, Kessler M, Papaspiliopoulos O. Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model. Physical Review E. Statistical, Nonlinear, and Soft Matter Physics. 2012;86(4):041114. https://doi.org/10.1103/PhysRevE.86.041114

Author

Jensen, Anders Christian ; Ditlevsen, Susanne ; Kessler , Mathieu ; Papaspiliopoulos , Omiros. / Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model. I: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics. 2012 ; Bind 86, Nr. 4. s. 041114.

Bibtex

@article{137fe6272aa24e649a17c79d60b01c2b,
title = "Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model",
abstract = "Excitability is observed in a variety of natural systems, such as neuronal dynamics, cardiovascular tissues, or climate dynamics. The stochastic FitzHugh-Nagumo model is a prominent example representing an excitable system. To validate the practical use of a model, the first step is to estimate model parameters from experimental data. This is not an easy task because of the inherent nonlinearity necessary to produce the excitable dynamics, and because the two coordinates of the model are moving on different time scales. Here we propose a Bayesian framework for parameter estimation, which can handle multidimensional nonlinear diffusions with large time scale separation. The estimation method is illustrated on simulated data.",
author = "Jensen, {Anders Christian} and Susanne Ditlevsen and Mathieu Kessler and Omiros Papaspiliopoulos",
year = "2012",
doi = "10.1103/PhysRevE.86.041114",
language = "English",
volume = "86",
pages = "041114",
journal = "Physical Review E",
issn = "2470-0045",
publisher = "American Physical Society",
number = "4",

}

RIS

TY - JOUR

T1 - Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model

AU - Jensen, Anders Christian

AU - Ditlevsen, Susanne

AU - Kessler , Mathieu

AU - Papaspiliopoulos , Omiros

PY - 2012

Y1 - 2012

N2 - Excitability is observed in a variety of natural systems, such as neuronal dynamics, cardiovascular tissues, or climate dynamics. The stochastic FitzHugh-Nagumo model is a prominent example representing an excitable system. To validate the practical use of a model, the first step is to estimate model parameters from experimental data. This is not an easy task because of the inherent nonlinearity necessary to produce the excitable dynamics, and because the two coordinates of the model are moving on different time scales. Here we propose a Bayesian framework for parameter estimation, which can handle multidimensional nonlinear diffusions with large time scale separation. The estimation method is illustrated on simulated data.

AB - Excitability is observed in a variety of natural systems, such as neuronal dynamics, cardiovascular tissues, or climate dynamics. The stochastic FitzHugh-Nagumo model is a prominent example representing an excitable system. To validate the practical use of a model, the first step is to estimate model parameters from experimental data. This is not an easy task because of the inherent nonlinearity necessary to produce the excitable dynamics, and because the two coordinates of the model are moving on different time scales. Here we propose a Bayesian framework for parameter estimation, which can handle multidimensional nonlinear diffusions with large time scale separation. The estimation method is illustrated on simulated data.

U2 - 10.1103/PhysRevE.86.041114

DO - 10.1103/PhysRevE.86.041114

M3 - Journal article

C2 - 23214536

VL - 86

SP - 041114

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 4

ER -

ID: 41890242