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Long memory based approximation of filtering in non linear switching systems

Abstract : In this paper we consider conditionally Gaussian state space models with Markovian switches and we propose a new method of approximating the optimal solution by the use of Markov chains hidden with long memory noise. We show through experiments that our method can be more efficient than the classical particle filter based approximation. Keywords: Conditionally Gaussian state space model, Markov switching, Markov chains hidden with long memory noise, Expectation-Maximization, Iterative conditional estimation
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01354810
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Friday, August 19, 2016 - 3:20:22 PM
Last modification on : Wednesday, September 30, 2020 - 3:25:21 AM

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  • HAL Id : hal-01354810, version 1

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Noufel Abbassi, Wojciech Pieczynski. Long memory based approximation of filtering in non linear switching systems. SMTDA 2010 : Stochastic Modeling Techniques and Data Analysis International Conference, Jun 2010, Chania, Greece. ⟨hal-01354810⟩

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