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

Ido Nevat; Ami Wiesel; Jinhong Yuan; Yonina C. Eldart

doi:10.1109/CISS.2007.4298274

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 proceeding › Conference contribution › peer-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 language	English
Title of host publication	Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	63-67
Number of pages	5
ISBN (Print)	1424410371, 9781424410378
DOIs	https://doi.org/10.1109/CISS.2007.4298274
State	Published - 2007
Externally published	Yes
Event	41st Annual Conference on Information Sciences and Systems, CISS 2007 - Baltimore, MD, United States Duration: 14 Mar 2007 → 16 Mar 2007

Publication series

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

Conference

Conference	41st Annual Conference on Information Sciences and Systems, CISS 2007
Country/Territory	United States
City	Baltimore, MD
Period	14/03/07 → 16/03/07

Access to Document

10.1109/CISS.2007.4298274

Cite this

Nevat, I., Wiesel, A., Yuan, J., & Eldart, Y. C. (2007). Maximum a-posteriori estimation in linear models with a Gaussian model matrix. In Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings (pp. 63-67). Article 4298274 (Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2007.4298274

Nevat, Ido ; Wiesel, Ami ; Yuan, Jinhong et al. / Maximum a-posteriori estimation in linear models with a Gaussian model matrix. Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2007. pp. 63-67 (Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings).

@inproceedings{1289870cacef451497ee5e46e319e360,

title = "Maximum a-posteriori estimation in linear models with a Gaussian model matrix",

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).",

author = "Ido Nevat and Ami Wiesel and Jinhong Yuan and Eldart, {Yonina C.}",

year = "2007",

doi = "10.1109/CISS.2007.4298274",

language = "אנגלית",

isbn = "1424410371",

series = "Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "63--67",

booktitle = "Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings",

address = "ארצות הברית",

note = "41st Annual Conference on Information Sciences and Systems, CISS 2007 ; Conference date: 14-03-2007 Through 16-03-2007",

}

Nevat, I, Wiesel, A, Yuan, J & Eldart, YC 2007, Maximum a-posteriori estimation in linear models with a Gaussian model matrix. in Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings., 4298274, Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 63-67, 41st Annual Conference on Information Sciences and Systems, CISS 2007, Baltimore, MD, United States, 14/03/07. https://doi.org/10.1109/CISS.2007.4298274

Maximum a-posteriori estimation in linear models with a Gaussian model matrix. / Nevat, Ido; Wiesel, Ami; Yuan, Jinhong et al.
Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2007. p. 63-67 4298274 (Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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).

AB - 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).

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Nevat I, Wiesel A, Yuan J, Eldart YC. Maximum a-posteriori estimation in linear models with a Gaussian model matrix. In Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2007. p. 63-67. 4298274. (Forty-first Annual Conference on Information Sciences and Systems, CISS 2007 - Proceedings). doi: 10.1109/CISS.2007.4298274

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

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