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By introducing Crossing functions and hyper-parameters I show that theBayesian interpretation of the Crossing Statistics [1] can be used triviallyfor the purpose of model selection among cosmological models. In this approachto falsify a cosmological model there is no need to compare it with othermodels or assume any particular form of parametrization for the cosmologicalquantities like luminosity distance, Hubble parameter or equation of state ofdark energy. Instead, hyper-parameters of Crossing functions perform asdiscriminators between correct and wrong models. Using this approach one canfalsify any assumed cosmological model without putting priors on the underlyingactual model of the universe and its parameters, hence the issue of dark energyparametrization is resolved. It will be also shown that the sensitivity of themethod to the intrinsic dispersion of the data is small that is anotherimportant characteristic of the method in testing cosmological models dealingwith data with high uncertainties.

Crossing Statistic: Bayesian interpretation, model selection and resolving dark energy parametrization problem