Maximum a-posteriori estimation in linear models with a random Gaussian model matrix: A Bayesian-EM approach

Ido Nevat, Gareth W. Peters, Jinhong Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper considers the problem of Bayesian estimation of a Gaussian vector in a linear model with random Gaussian uncertainty in the mixing matrix. The maximum a-posteriori estimator is derived for this model using the Bayesian expectation-maximization. It is demonstrated that the solution forms an elegant and simple iteration which can be easily implemented. Finally, the estimator developed is considered in the context of near-Gaussian-digitally modulated signals under channel uncertainty, where it is shown that the MAP estimator outperforms the standard linear MMSE estimator in terms of mean square error (MSE) and bit error rate (BER).
Original languageEnglish
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
Pages2889-2892
Number of pages4
ISBN (Print)9781424414833
DOIs
Publication statusPublished - 12 May 2008
Event33rd IEEE International Conference on Acoustics, Speech and Signal Processing 2008 - Las Vegas, NV, USA, Las Vegas, United States
Duration: 30 Mar 20084 Apr 2008

Conference

Conference33rd IEEE International Conference on Acoustics, Speech and Signal Processing 2008
Abbreviated titleICASSP 2008
CountryUnited States
CityLas Vegas
Period30/03/084/04/08

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  • Cite this

    Nevat, I., Peters, G. W., & Yuan, J. (2008). Maximum a-posteriori estimation in linear models with a random Gaussian model matrix: A Bayesian-EM approach. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 2889-2892). IEEE. https://doi.org/10.1109/ICASSP.2008.4518253