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Bayesian probability is one of the different interpretations of the concept of probability and belongs to the category of evidential probabilities. The Bayesian interpretation of probability can be seen as an extension of logic that enables reasoning with uncertain statements. To evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new relevant data. The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. Bayesian probability interprets the concept of probability as "a measure of a state of knowledge",^{[1]} in contrast to interpreting it as a frequency or a "propensity" of some phenomenon.
"Bayesian" refers to the 18th century mathematician and theologian Thomas Bayes (1702–1761), who provided the first mathematical treatment of a nontrivial problem of Bayesian inference.^{[2]} Nevertheless, it was the French mathematician PierreSimon Laplace (1749–1827) who pioneered and popularized what is now called Bayesian probability.^{[3]}
Broadly speaking, there are two views on Bayesian probability that interpret the state of knowledge concept in different ways. According to the objectivist view, the rules of Bayesian statistics can be justified by requirements of rationality and consistency and interpreted as an extension of logic.^{[1]}^{[4]} According to the subjectivist view, the state of knowledge measures a "personal belief".^{[5]} Many modern machine learning methods are based on objectivist Bayesian principles.^{[6]} In the Bayesian view, a probability is assigned to a hypothesis, whereas under the frequentist view, a hypothesis is typically tested without being assigned a probability.
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