A subspace-based variational Bayesian method

This paper is devoted to an improved variational Bayesian method. Actually, variational Bayesian issue can be seen as a convex functional optimization problem. Our main contribution is the adaptation of subspace optimization methods into the functional space involved in this problem. We highlight the efficiency of our methodology on a linear inverse problem with a sparse prior. Comparisons with classical Bayesian methods through a numerical example show the notable improved computation time.