Selecting a subset of variables for linear models remains an active area of research. This
article reviews many of the recent contributions to the Bayesian model selection and …

We propose a new data-augmentation strategy for fully Bayesian inference in models with
binomial likelihoods. The approach appeals to a new class of Pólya–Gamma distributions …

In this note we derive a simple Bayesian sampler for linear regression with the horseshoe
hierarchy. A new interpretation of the horseshoe model is presented, and extensions to …

We use Lévy processes to generate joint prior distributions for a location parameter β=(β1,...,
βp) as p grows large. This approach, which generalizes normal scale-mixture priors to an …

This paper argues that the half-Cauchy distribution should replace the inverse-Gamma
distribution as a default prior for a top-level scale parameter in Bayesian hierarchical …

We provide a review of prior distributions for objective Bayesian analysis. We start by
examining some foundational issues and then organize our exposition into priors for: i) …

The goal of this paper is to contrast and survey the major advances in two of the most
commonly used high-dimensional techniques, namely, the Lasso and horseshoe …

A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from
spectrally-compressed measurements. The wavelength-dependent data are coded and then …

Proximal algorithms are useful for obtaining solutions to difficult optimization problems,
especially those involving nonsmooth or composite objective functions. A proximal algorithm …

We propose a Bayesian approach to regression with a scalar response on vector and tensor
covariates. Vectorization of the tensor prior to analysis fails to exploit the structure, often …