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:

Jeffreys prior - Wikipedia Jump to content From Wikipedia, the free encyclopedia Non-informative prior distribution In Bayesian statistics , the Jeffreys prior is a non-informative prior distribution for a parameter space . Named after Sir Harold Jeffreys , [ 1 ] 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

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