DPM pour l'inférence dans les modèles dynamiques non linéaires avec des bruits de mesure alpha-stable

Nouha Jaoua 1 Emmanuel Duflos 2, 1, * Philippe Vanheeghe 2, 1
* Corresponding author
1 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
Abstract : Stable random variables are often use to model impulsive noise; Recently it has be shown that communication at very high frequency suffer from such a noise. Stable noise cannot however be considered as usual noise in estimation processes because the variance does not usually exists nor an analytic expression for the probability density function. In this work we show how to manage such a problem using a bayesian nonparametric approach. We develop a Sequential Monte Carlo based algorithm to realize the estimation in a non linear dynamical system. The measurement noise is a non-stationnary stable process and it is modeled using a Dirichlet Process Mixture.
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https://hal.archives-ouvertes.fr/hal-00713857
Contributor : Emmanuel Duflos <>
Submitted on : Monday, July 2, 2012 - 6:25:50 PM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM

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  • HAL Id : hal-00713857, version 1

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Nouha Jaoua, Emmanuel Duflos, Philippe Vanheeghe. DPM pour l'inférence dans les modèles dynamiques non linéaires avec des bruits de mesure alpha-stable. 44ème Journées de Statistique, May 2012, Bruxelles, Belgique. pp.1-4. ⟨hal-00713857⟩

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