Multiscale Piecewise Deterministic Markov Process in infinite dimension: central limit theorem and Langevin approximation
ESAIM: Probability and Statistics, Tome 18 (2014), pp. 541-569.

In [A. Genadot and M. Thieullen, Averaging for a fully coupled piecewise-deterministic markov process in infinite dimensions. Adv. Appl. Probab. 44 (2012) 749-773], the authors addressed the question of averaging for a slow-fast Piecewise Deterministic Markov Process (PDMP) in infinite dimensions. In the present paper, we carry on and complete this work by the mathematical analysis of the fluctuations of the slow-fast system around the averaged limit. A central limit theorem is derived and the associated Langevin approximation is considered. The motivation for this work is the study of stochastic conductance based neuron models which describe the propagation of an action potential along a nerve fiber.

DOI : 10.1051/ps/2013051
Classification : 60B12, 60J75, 35K57
Mots clés : piecewise deterministic Markov process, averaging principle, neuron model
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Genadot, A.; Thieullen, M. Multiscale Piecewise Deterministic Markov Process in infinite dimension: central limit theorem and Langevin approximation. ESAIM: Probability and Statistics, Tome 18 (2014), pp. 541-569. doi : 10.1051/ps/2013051. http://archive.numdam.org/articles/10.1051/ps/2013051/

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