Bayesian model selection

In Bayesian Probability, Bayesian model selection is a method for choosing the best hypothesis (model) out of a set of competing models which best explains some observed data. Best here is measured by the Bayesian posterior odds ratio of the winner compared against all other candidates in the competition. The posterior odds ratio is the product of the Bayes Factor and the Bayes prior odds ratio.