Laplace priors and spatial inhomogeneity in Bayesian inverse problems
Laplace priors and spatial inhomogeneity in Bayesian inverse problems
Spatially inhomogeneous functions, which may be smooth in some regions and rough in other regions, are modelled naturally in a Bayesian manner using so-called Besov priors which are given by random wavelet expansions with Laplace-distributed coefficients. This paper studies theoretical guarantees for such prior measures - specifically, we examine their …