Inverse problems are often ill-posed, with solutions that depend sensitively
on data. In any numerical approach to the solution of such problems,
regularization of some form is needed to counteract the resulting instability.
This paper is based on an approach to regularization, employing a Bayesian
formulation of the problem, which leads to a notion of well-posedness for
inverse problems, at the level of probability measures.