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Название: Jaynes's maximum entropy prescription and probability theory
Авторы: Kenneth Friedman, Abner Shimony
Jaynes's prescription of maximizing the information-theoretic entropy is applied in a special situation to determine a certain set of posterior probabilities (when evidence fixing the expected value of a dynamical variable is given) and also the corresponding set of prior probabilities (when this evidence is not given). It is shown that the resulting values of these probabilities are inconsistent with the principles of probability theory. Three possible ways of avoiding this inconsistency are briefly discussed.