Bayesian Sparse Estimation Using Double Lomax Priors

Gu, Xiaojing; Leung, Henry; Gu, Xingsheng

Bayesian Sparse Estimation Using Double Lomax Priors

dc.contributor.author	Gu, Xiaojing
dc.contributor.author	Leung, Henry
dc.contributor.author	Gu, Xingsheng
dc.date.accessioned	2018-09-27T11:40:09Z
dc.date.available	2018-09-27T11:40:09Z
dc.date.issued	2013-08-27
dc.date.updated	2018-09-27T11:40:09Z
dc.description.abstract	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.
dc.description.version	Peer Reviewed
dc.identifier.citation	Xiaojing Gu, Henry Leung, and Xingsheng Gu, “Bayesian Sparse Estimation Using Double Lomax Priors,” Mathematical Problems in Engineering, vol. 2013, Article ID 176249, 17 pages, 2013. doi:10.1155/2013/176249
dc.identifier.doi	https://doi.org/10.1155/2013/176249
dc.identifier.uri	http://hdl.handle.net/1880/108255
dc.identifier.uri	https://doi.org/10.11575/PRISM/44468
dc.language.rfc3066	en
dc.rights.holder	Copyright © 2013 Xiaojing Gu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.title	Bayesian Sparse Estimation Using Double Lomax Priors
dc.type	Journal Article