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Theses

Bayesian state estimation in partially observable Markov processes

Abstract : This thesis addresses the Bayesian estimation of hybrid-valued state variables in time series. The probability density function of a hybrid-valued random variable has a finite-discrete component and a continuous component. Diverse general algorithms for state estimation in partially observable Markov processesare introduced. These algorithms are compared with the sequential Monte-Carlo methods from a theoretical and a practical viewpoint. The main result is that the proposed methods require less processing time compared to the classic Monte-Carlo methods
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Ivan Gorynin. Bayesian state estimation in partially observable Markov processes. Signal and Image Processing. Université Paris-Saclay, 2017. English. ⟨NNT : 2017SACLL009⟩. ⟨tel-01705284⟩

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