DocumentCode
1806097
Title
Adaptive algorithms and Markov chain Monte Carlo methods
Author
Solo, Victor
Author_Institution
Dept. of Stat., Macquarie Univ., North Ryde, NSW, Australia
Volume
2
fYear
1999
fDate
1999
Firstpage
1775
Abstract
Many signal processing and control problems are complicated by the presence of unobserved variables and/or auxiliary variables measured with error. In nonlinear settings this causes problems in constructing adaptive parameter estimators. In off-line situations so-called Markov chain Monte Carlo methods have recently become popular for solving these kinds of problems. In this paper we explore the development of online Markov chain Monte Carlo techniques for adaptive parameter estimation
Keywords
Markov processes; Monte Carlo methods; adaptive signal processing; parameter estimation; signal processing; Markov chain; Monte Carlo methods; convergence; logistic regression; parameter estimation; signal processing; Adaptive algorithm; Adaptive signal processing; Approximation algorithms; Computer errors; Convergence; Error analysis; Logistics; Parameter estimation; Signal processing algorithms; Signal to noise ratio;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1999. Proceedings of the 38th IEEE Conference on
Conference_Location
Phoenix, AZ
ISSN
0191-2216
Print_ISBN
0-7803-5250-5
Type
conf
DOI
10.1109/CDC.1999.830890
Filename
830890
Link To Document