Kantas, N and Doucet, A and Singh, SS and MacIejowski, JM (2009) An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models. IFAC Proceedings Volumes (IFAC-PapersOnline), 15. pp. 774-785. ISSN 1474-6670Full text not available from this repository.
Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models. We discuss the advantages and limitations of these methods. © 2009 IFAC.
|Uncontrolled Keywords:||General state-space models Hidden Markov models Parameter estimation Sequential Monte Carlo|
|Divisions:||Div F > Control|
Div F > Signal Processing and Communications
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 12:03|
|Last Modified:||08 Dec 2014 02:31|