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Bayesian Sparse Estimation Using Double Lomax Priors
Joint Authors
Gu, Xiaojing
Leung, Henry
Gu, Xingsheng
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-08-27
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Sparsity-promoting prior along with Bayesian inference is an effective approach in solving sparse linear models (SLMs).
In this paper, we first introduce a new sparsity-promoting prior coined as Double Lomax prior, which corresponds to a three-level hierarchical model, and then we derive a full variational Bayesian (VB) inference procedure.
When noninformative hyperprior is assumed, we further show that the proposed method has one more latent variable than the canonical automatic relevance determination (ARD).
This variable has a smoothing effect on the solution trajectories, thus providing improved convergence performance.
The effectiveness of the proposed method is demonstrated by numerical simulations including autoregressive (AR) model identification and compressive sensing (CS) problems.
American Psychological Association (APA)
Gu, Xiaojing& Leung, Henry& Gu, Xingsheng. 2013. Bayesian Sparse Estimation Using Double Lomax Priors. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-17.
https://search.emarefa.net/detail/BIM-1031720
Modern Language Association (MLA)
Gu, Xiaojing…[et al.]. Bayesian Sparse Estimation Using Double Lomax Priors. Mathematical Problems in Engineering No. 2013 (2013), pp.1-17.
https://search.emarefa.net/detail/BIM-1031720
American Medical Association (AMA)
Gu, Xiaojing& Leung, Henry& Gu, Xingsheng. Bayesian Sparse Estimation Using Double Lomax Priors. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-17.
https://search.emarefa.net/detail/BIM-1031720
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1031720