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Bayesian model selection (view source)
Revision as of 14:14, December 8, 2007
, 14:14, December 8, 2007no edit summary
'''Bayesian model selection''' is a technique in probability theory for choosing a [[hypothesis]] (model) to fit observed data.
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.
[[category:statistics]]
[[category:statistics]]