Difference between revisions of "Bayesian model selection"
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| − | In [[Bayesian Probability]], '''Bayesian model selection''' is a method for choosing the best [[hypothesis]] | + | 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. |
[[category:Probability]] | [[category:Probability]] | ||
Revision as of 16:22, December 8, 2007
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.