CUED Publications database

On particle methods for parameter estimation in state-space models

Kantas, N and Doucet, A and Singh, SS and Maciejowski, J and Chopin, N (2015) On particle methods for parameter estimation in state-space models. Statistical Science, 30. pp. 328-351. ISSN 0883-4237

Full text not available from this repository.


Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics, information engineering and signal processing. Particle methods, also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical approximations to the associated state inference problems. However, in most applications, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard particle methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive review of particle methods that have been proposed to perform static parameter estimation in state-space models. We discuss the advantages and limitations of these methods and illustrate their performance on simple models.

Item Type: Article
Divisions: Div F > Control
Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 18:59
Last Modified: 09 Sep 2021 00:31
DOI: 10.1214/14-STS511