Markov chain Monte Carlo methods with applications to signal processing

Authors
Citation
Wj. Fitzgerald, Markov chain Monte Carlo methods with applications to signal processing, SIGNAL PROC, 81(1), 2001, pp. 3-18
Citations number
34
Categorie Soggetti
Eletrical & Eletronics Engineeing
Journal title
SIGNAL PROCESSING
ISSN journal
01651684 → ACNP
Volume
81
Issue
1
Year of publication
2001
Pages
3 - 18
Database
ISI
SICI code
0165-1684(200101)81:1<3:MCMCMW>2.0.ZU;2-T
Abstract
The last five years have witnessed a really significant increase in the awa reness of numerical Bayesian methods, both in Statistics and in Signal Proc essing. It is now clear that many problems that could only be addressed usi ng ad hoc methods, because of their complexity, can now be solved and these solutions can be applied to almost all areas of data and signal processing . Bayesian methods have been popular for decades. However, various approxim ations have been required in order to make progress because most of the int egrations required within the framework have no analytical solutions apart from some simple models which usually involve Gaussian and linearity assump tions. This explains why sub-optimal, ad hoc approximations have been devel oped. The aim of this paper is to set out the foundations upon which modern numerical Bayesian methods are based, give one application to missing data in audio restoration and then give references to application areas that ca n be addressed. (C) 2001 Elsevier Science B.V. All rights reserved.