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发表于 2013-2-28 01:10:35
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Bayesian methodology| In general, Bayesian methods are characterized by the following concepts and procedures:
1)The use of hierarchical models and marginalization over the values of nuisance parameters. In most cases, the computation is intractable, but good approximations can be obtained using Markov chain Monte Carlo methods.
2)The sequential use of the Bayes' formula: when more data become available after calculating a posterior distribution, the posterior becomes the next prior.
3) For the frequentist a hypothesis is a proposition (which must be either true or false), so that the frequentist probability of a hypothesis is either one or zero. In Bayesian statistics, a probability can be assigned to a hypothesis that can differ from 0 or 1 if the truth value is uncertain.
http://en.wikipedia.org/wiki/Bayesian_probability | 
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