In Bayesian analysis, the posterior distribution is the conditional probability distribution
of an unknown parameter after observed data have been
taken into account. If 
is the density of a prior
distribution and 
is the likelihood
function for observed data 
, then the posterior density is
 |
The denominator normalizes the posterior density and is replaced by a sum when the parameter is discrete. Thus the posterior distribution combines the prior distribution with the information supplied by the data.
