High Quality Content by WIKIPEDIA articles! In Bayesian inference, a prior probability distribution, often called simply the prior, is a probability distribution representing knowledge or belief about an unknown quantity a priori, that is, before any data have been observed P(A). The unknown quantity could be a parameter, hypothesis or latent variable. The posterior probability is then the conditional probability taking the data into account P(A | B). This is computed from the prior and the likelihood function via Bayes' theorem.

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