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In Bayesian statistics, the posterior probability of a random event or an uncertain proposition is the conditional probability that is assigned after the relevant evidence or background is taken into account. Similarly, the posterior probability distribution is the probability distribution of an unknown quantity, treated as a random variable, conditional on the evidence obtained from an experiment or survey. "Posterior", in this context, means after taking into account the relevant evidence related to the particular case being examined. ==Definition== The posterior probability is the probability of the parameters given the evidence : . It contrasts with the likelihood function, which is the probability of the evidence given the parameters: . The two are related as follows: Let us have a prior belief that the probability distribution function is and observations with the likelihood , then the posterior probability is defined as : The posterior probability can be written in the memorable form as :. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「posterior probability」の詳細全文を読む スポンサード リンク
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