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 -..Full 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, 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)|
|Divisions:||Div F > Signal Processing and Communications|
Div F > Control
|Depositing User:||Unnamed user with email email@example.com|
|Date Deposited:||09 Dec 2016 17:36|
|Last Modified:||18 Jan 2017 23:20|