An EM-based estimation algorithm for a class of systems promoting sparsity

Boris I. Godoy, Rodrigo Carvajal, Juan C. Aguero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2013 European Control Conference, ECC 2013
PublisherIEEE Computer Society
Pages2398-2403
Number of pages6
ISBN (Print)9783033039629
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 12th European Control Conference, ECC 2013 - Zurich, Switzerland
Duration: 17 Jul 201319 Jul 2013

Publication series

Name2013 European Control Conference, ECC 2013

Conference

Conference2013 12th European Control Conference, ECC 2013
Country/TerritorySwitzerland
CityZurich
Period17/07/1319/07/13

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