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.[1] 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, a priori probabilities are known as "uninformative priors" or "objective priors"; note that "prior probability" is a broader concept.
See also
References
- ^ Mood A.M., Graybill F.A., Boes D.C. (1974) Introduction to the Theory of Statistics (3rd Edition). McGraw-Hill. Section 2.2
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