Maximum a-posteriori estimation in linear models with a Gaussian model matrix

Ido Nevat*, Ami Wiesel, Jinhong Yuan, Yonina C. Eldart

*Corresponding author for this work

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

3 Scopus citations

Abstract

We consider the Bayesian inference of a random Gaussian vector in a linear model with a Gaussian model matrix. We derive the maximum a-posteriori (MAP) estimator for this model and show that it can be found using a simple line search over a unimodal function that can be efficiently evaluated. Next, we discuss the application of this estimator in the context of near-optimal detection of near-Ganssian-digitally modulated signals and demonstrate through simulations that the MAP estimator outperforms the standard linear MMSK estimator in terms of mean square error (MSK) and bit error rate (HER).

Original languageAmerican English
Title of host publicationForty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages63-67
Number of pages5
ISBN (Print)1424410371, 9781424410378
DOIs
StatePublished - 2007
Externally publishedYes
Event41st Annual Conference on Information Sciences and Systems, CISS 2007 - Baltimore, MD, United States
Duration: 14 Mar 200716 Mar 2007

Publication series

NameForty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings

Conference

Conference41st Annual Conference on Information Sciences and Systems, CISS 2007
Country/TerritoryUnited States
CityBaltimore, MD
Period14/03/0716/03/07

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