Jeffreys Prior

In Bayesian probability, the Jeffreys prior, named after Harold Jeffreys, is a non-informative (objective) prior distribution on parameter space that 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.