  A complete solution for an inverse problem needs five main steps: choice of
basis functions for discretization, determination of the order of the model,
estimation of the hyperparameters, estimation of the solution, and finally,
characterization of the proposed solution. Many works have been done for the
three last steps. The first two have been neglected for a while, in part due to
the complexity of the problem. However, in many inverse problems, particularly
when the number of data is very low, a good choice of the basis functions and a
good selection of the order become primary. In this paper, we first propose a
complete solution within a Bayesian framework. Then, we apply the proposed
method to an inverse elastic electron scattering problem.
