Jeffreys prior

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In Bayesian statistics, the Jeffreys prior is a non-informative prior distribution for a parameter space. Named after Sir Harold Jeffreys, its density function is proportional to the square root of the determinant of the Fisher information matrix: p ( θ ) ∝ | I ( θ ) | 1 / 2 . {\displaystyle p\left(\theta \right)\propto \left|I(\theta )\right|^{1/2}.\,} It has the key feature that it is invariant under a change of coordinates for the parameter vector θ {\textstyle \theta } . That is, the relative probability assigned to a volume of a probability space using a Jeffreys prior will be the same regardless...

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