Bayesian Sparse Estimation Using Double Lomax Priors

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.