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