Constraining power of open likelihoods, made prior-independent
  One of the most criticized features of Bayesian statistics is the fact that
credible intervals, especially when open likelihoods are involved, may strongly
depend on the prior shape and range. Many analyses involving open likelihoods
are affected by the eternal dilemma of choosing between linear and logarithmic
prior, and in particular in the latter case the situation is worsened by the
dependence on the prior range under consideration. In this letter, we revive a
simple method to obtain constraints that depend neither on the prior shape nor
range and, using the tools of Bayesian model comparison, extend it to overcome
the possible dependence of the bounds on the choice of free parameters in the
numerical analysis. An application to the case of cosmological bounds on the
sum of the neutrino masses is discussed as an example.
