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Bayesian statistics is a probability theory developed by Thomas Bayes in the 18th century. The theory is based on the existence of prior probabilities, which may change as new relevant information is taken into account. The resulting probabilities are called posterior probabilities.
The intuition is formally described by Bayes’ theorem, which states that the conditional probability of an event A, given the occurrence of an event B, is equal to the probability of B given A, multiplied by the ratio between the probability of A and the probability of B.
The approach is opposed to so-called classical, or frequentist, statistics, in which probabilities are estimated by repeatedly observing the behaviour of a random event and measuring its frequency.
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