A Bayesian Approach to Gravitational Lens Model Selection, SCMAV
  proceeding
  Strong gravitational lenses are unique cosmological probes. These produce
multiple images of a single source. Whether a single galaxy, a group or a
cluster, extracting cosmologically relevant information requires an accurate
modeling of the lens mass distribution. A variety of models are available to
this purpose, nevertheless discrimination between them as primarely relied on
the quality of fit without accounting for the size of the prior model parameter
space. This is a problem of model selection that we address in the Bayesian
statistics framework by evaluating Bayes' factors. Using simple test cases, we
show that the assumption of more complicate lens models may not be justified
given the level of accuracy of the available data.
