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 .
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 .
Source: Wikipedia "Jeffreys prior" · CC BY-SA 4.0
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