Crossing Statistic: Bayesian interpretation, model selection and
  resolving dark energy parametrization problem
  By introducing Crossing functions and hyper-parameters I show that the
Bayesian interpretation of the Crossing Statistics [1] can be used trivially
for the purpose of model selection among cosmological models. In this approach
to falsify a cosmological model there is no need to compare it with other
models or assume any particular form of parametrization for the cosmological
quantities like luminosity distance, Hubble parameter or equation of state of
dark energy. Instead, hyper-parameters of Crossing functions perform as
discriminators between correct and wrong models. Using this approach one can
falsify any assumed cosmological model without putting priors on the underlying
actual model of the universe and its parameters, hence the issue of dark energy
parametrization is resolved. It will be also shown that the sensitivity of the
method to the intrinsic dispersion of the data is small that is another
important characteristic of the method in testing cosmological models dealing
with data with high uncertainties.
