DocumentCode
646429
Title
An EM-based estimation algorithm for a class of systems promoting sparsity
Author
Godoy, Boris I. ; Carvajal, Rodrigo ; Aguero, Juan C.
Author_Institution
Sch. of Electr. Eng. & Comput. Sci., Univ. of Newcastle, Callaghan, NSW, Australia
fYear
2013
fDate
17-19 July 2013
Firstpage
2398
Lastpage
2403
Abstract
In this paper we propose a Maximum a Posteriori (MAP) approach for estimating a random sparse parameter vector in the presence of nonlinearities of unknown parameters. In this Bayesian approach, the a priori probability distribution for the parameter vector is utilised as a mechanism to promote sparsity. We solve this identification problem by using a generalized Expectation Maximization algorithm in a MAP framework.
Keywords
Bayes methods; expectation-maximisation algorithm; Bayesian approach; EM based estimation algorithm; Expectation Maximization Algorithm; MAP framework; maximum a posteriori approach; parameter vector; probability distribution; random sparse parameter vector; sparsity system; Bayes methods; Equations; Maximum likelihood estimation; Noise measurement; Parameter estimation; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (ECC), 2013 European
Conference_Location
Zurich
Type
conf
Filename
6669839
Link To Document