Changes

By integrating out the model class parameters for comparitive classes of likelihood models, one is  effectively comparing the strengths of the predictions of the two model classes concerning the observed data. I.e., in determining which model class has the largest [[posterior odds ratio]] (of which the ''Bayes factor'' is a principle component) when compared against all others, the model class or [[mathematical model]] which best explains the data is determined.  It is then left to determine the best inference as to the values of that models parameters via [[parameter estimation]].

By integrating out the model class parameters for comparative classes of likelihood models, one is  effectively comparing the weighted strengths of the predictions of the two model classes concerning the observed data. In particular, in determining which model class has the largest [[posterior odds ratio]] (of which the ''Bayes factor'' is a principle component) when compared against all others, the model class or [[mathematical model]] which best explains the data among the competition  is determined.  It is then left to determine the best inference as to the values of that models parameters via [[parameter estimation]].

[[Category:Probability]]

[[Category:Probability]]