Gu, XiaojingLeung, HenryGu, Xingsheng2018-09-272018-09-272013-08-27Xiaojing 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:10.1155/2013/176249http://hdl.handle.net/1880/108255https://doi.org/10.11575/PRISM/44468Sparsity-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.Journal Article2018-09-27enCopyright © 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.https://doi.org/10.1155/2013/176249