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Jeffreys prior : ウィキペディア英語版 | Jeffreys prior In Bayesian probability, the Jeffreys prior, named after Sir Harold Jeffreys, is a non-informative (objective) prior distribution for a parameter space; it is proportional to the square root of the determinant of the Fisher information: : It has the key feature that it is invariant under reparameterization of the parameter vector . This makes it of special interest for use with ''scale parameters''.〔Jaynes, E. T. (1968) "Prior Probabilities", ''IEEE Trans. on Systems Science and Cybernetics'', SSC-4, 227 (pdf ).〕 == Reparameterization ==
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