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Published online by Cambridge University Press: 19 June 2023
Classical Bayesian methodology is based on the following three principles:
(i) individuals have degrees of belief which, measured in the closed unit interval, and subject to a mild consistency constraint, are fonnally probabilities.
(ii) belief functions are updated with the acquisition of new evidence by Bayesian conditionalisation. In other words, if B is learned to be true, then your new probability function P′ takes the value P′(A) = P(A/B) on every A in domain P′, where P is your probability function prior to learning B.
(iii) where Hi is a statistical hypothesis and E sample data, the tenns P(E|Hi) in Bayes’ Theorem calculations are set equal to the probability assigned E by Hi.