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A priori (statistics)
In statistics, a priori knowledge refers to prior knowledge about a population, rather than that estimated by recent observation. It is common in Bayesian inference to make inferences conditional upon this knowledge, and the integration of a priori knowledge is the central difference between the Bayesian and Frequentist approach to statistics. We need not be 100 ertain about something before it can be considered A priori knowledge, but conducting estimation conditional upon assumptions for which there is little evidence should be avoided. A priori knowledge often consists of knowledge of the domain of a parameter (for example, that it is positive) that can be incorporated to improve an estimate. Within this domain the distribution is usually assumed to be uniform in order to take advantage of certain theoretical results (most importantly the central limit theorem).
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