Changes

In [[Bayesian Probability]], '''Bayesian model selection''' is a method for choosing the best [[hypothesis]] posed as a [[probabilistic likelihood 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.

In [[Bayesian Probability]], '''Bayesian model selection''' is a method for choosing the best [[hypothesis]] or [[model class]] posed as a [[probabilistic likelihood model]] out of a set of competing model classes 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. In short one is comparing the [[marginal likelihood]] of entire classes by marginalizing over their assocaited parameter values.  After the model class has been so selected, one can then go on to do [[parameter estimation]] to determine the best inference as to the values of the parameters of that particular model class.

[[category:Probability]]

[[category:Probability]]