Recursive Monte Carlo algorithms for parameter estimation in general state space models

Author

Andrieu, Christophe ; Doucet, A.

Author_Institution

Dept. of Math., Bristol Univ., UK

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

14

Lastpage

17

Abstract

We present new algorithms that aim at estimating the "static" parameters of a latent variable process in an on-line manner. This new class of on-line algorithms is inspired by Monte Carlo Markov chain (MCMC) methods whose use has been mainly restricted to static problems, i.e., for which the set of observations is fixed. The main interest of this new class of algorithms is that it combines MCMC and particle filtering techniques, for which extensive know-how and literature are now available

Keywords

Markov processes; Monte Carlo methods; filtering theory; parameter estimation; recursive estimation; signal processing; state-space methods; Markov chains; general state space models; latent variable process; on-line manner; parameter estimation; particle filtering; recursive Monte Carlo algorithms; signal processing; stochastic processes; Filtering algorithms; Mathematical model; Mathematics; Monte Carlo methods; Parameter estimation; Signal processing; Signal processing algorithms; State estimation; State-space methods; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on

Print_ISBN

0-7803-7011-2

Type

conf

DOI

10.1109/SSP.2001.955210

Filename

955210