  We present a promising method to learn physical parameters from a bayesian
inference, using modern tools to replace both our traditional fits and the way
errors are computed and propagated. A few models are built as illustrations for
a realistic case with Lattice QCD data, and appear to extract a lot of
information with good stability. We discuss the evaluation of these models with
either a fully bayesian approach or information criteria, as well as the
model-building challenges which remain to be solved.
