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The term ''a priori'' probability is used in distinguishing the ways in which values for probabilities can be obtained. In particular, an "''a priori'' probability" is derived purely by deductive reasoning.〔Mood A.M., Graybill F.A., Boes D.C. (1974) Introduction to the Theory of Statistics (3rd Edition). McGraw-Hill. Section 2.2 ((available online ))〕 One way of deriving ''a priori'' probabilities is the principle of indifference, which has the character of saying that, if there are ''N'' mutually exclusive and exhaustive events and if they are equally likely, then the probability of a given event occurring is 1/''N''. Similarly the probability of one of a given collection of ''K'' events is ''K''/''N''. One disadvantage of defining probabilities in the above way is that it applies only to finite collections of events. In Bayesian inference, the terms "uninformative priors" or "objective priors" refer to particular choices of ''a priori'' probabilities.〔E.g. Harold J. Price and Allison R. Manson, ("Uninformative priors for Bayes’ theorem" ), ''AIP Conf. Proc. 617, 2001〕 Note that "prior probability" is a broader concept. Similar to the distinction in philosophy between a priori and a posteriori, in Bayesian inference ''a priori'' denotes general knowledge about the data distribution before making an inference, while ''a posteriori'' denotes knowledge that incorporates the results of making an inference.〔.〕 ==References== 〔 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「a priori probability」の詳細全文を読む スポンサード リンク
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