Title :
Online sequential Monte Carlo EM algorithm
Author_Institution :
LTCI, Telecom ParisTech, Paris, France
Abstract :
Online (or recursive) estimation of fixed model parameters in general state-space models is a crucial but often difficult task. This paper is about likelihood-based point estimation, showing that an online EM (Expectation-Maximization) algorithm recently proposed for discrete hidden Markov models can be extended to more general settings, including non-linear non-Gaussian state-space models that necessitate the use of sequential Monte Carlo filtering approximations. The performance of the proposed online sequential Monte Carlo EM algorithm is illustrated on numerical examples.
Keywords :
Monte Carlo methods; approximation theory; expectation-maximisation algorithm; filtering theory; hidden Markov models; parameter estimation; discrete hidden Markov model; expectation-maximization algorithm; filtering approximation; online sequential Monte Carlo algorithm; parameter estimation; state-space model; Filtering; Hidden Markov models; Monte Carlo methods; Parameter estimation; Recursive estimation; Signal processing algorithms; Sliding mode control; Smoothing methods; State estimation; Tin;
Conference_Titel :
Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on
Conference_Location :
Cardiff
Print_ISBN :
978-1-4244-2709-3
Electronic_ISBN :
978-1-4244-2711-6
DOI :
10.1109/SSP.2009.5278646
