Bayesian statistics

Jeffreys prior

In Bayesian probability, the Jeffreys prior, named after Sir Harold Jeffreys, is a non-informative (objective) prior distribution for a parameter space; its density function is proportional to the square root of the determinant of the Fisher information matrix: It has the key feature that it is invariant under a change of coordinates for the parameter vector . That is, the relative probability assigned to a volume of a probability space using a Jeffreys prior will be the same regardless of the parameterization used to define the Jeffreys prior. This makes it of special interest for use with scale parameters. (Wikipedia).

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Related pages

Scale parameter | Exponential family | Minimum description length | Haar measure | Beta distribution | Arcsine distribution | Additive smoothing | Harold Jeffreys | Likelihood principle | Normalizing constant | Square root | Determinant | Dirichlet distribution | Poisson distribution | Bayesian probability | Unit sphere | Fisher information