A Bayesian model specifies a sampling distribution 
for observed data 
together with a prior distribution 
for the unknown parameter 
. Bayes' theorem gives
the posterior distribution
 |
where 
is the marginal likelihood. Bayesian model comparison uses these marginal likelihoods
or approximations such as the Bayesian
information criterion.
