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.

Read more about Jeffreys Prior:  Attributes, Minimum Description Length, Examples

Famous quotes containing the word prior:

    “A diff’rent cause, says Parson Sly,
    The same effect may give:
    Poor Lubin fears, that he shall die;
    His wife, that he may live.”
    —Matthew Prior (1664–1721)