Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

Sampling from various kinds of distribution is an issue of paramount importance in statistics
since it is often the key ingredient for constructing estimators, test procedures or confidence …

We study full Bayesian procedures for high-dimensional linear regression under sparsity
constraints. The prior is a mixture of point masses at zero and continuous distributions …

In many clinical studies, researchers are interested in parsimonious models that
simultaneously achieve consistent variable selection and optimal prediction. The resulting …

Recent decades have seen enormous improvements in computational inference for
statistical models; there have been competitive continual enhancements in a wide range of …

On the computational complexity of high-dimensional Bayesian variable selection Page 1 The
Annals of Statistics 2016, Vol. 44, No. 6, 2497–2532 DOI: 10.1214/15-AOS1417 © Institute of …

This paper presents a new Metropolis-adjusted Langevin algorithm (MALA) that uses
convex analysis to simulate efficiently from high-dimensional densities that are log-concave …

Optimal transport (OT) theory focuses, among all maps $ T:\mathbb {R}^ d\rightarrow\mathbb
{R}^ d $ that can morph a probability measure onto another, on those that are the``thriftiest'' …

We consider the problem of sampling from the posterior distribution of a $ d $-dimensional
coefficient vector $\boldsymbol {\theta} $, given linear observations $\boldsymbol …

Importance sampling (IS) is a powerful Monte Carlo methodology for the approximation of
intractable integrals, very often involving a target probability distribution. The performance of …