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:
“To John I owed great obligation;
But John, unhappily, thought fit
To publish it to all the nation:
Sure John and I are more than quit.”
—Matthew Prior (1664–1721)