CUED Publications database

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

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. In: 15th IFAC Symposium on System Identification, SYSID 2009, 2009-7-6 to 2009-7-8, Saint-Malo, France -..

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Abstract

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, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Div F > Control
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 12:27
Last Modified: 10 Nov 2014 01:05
DOI: