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:
“Less smooth than her Skin and less white than her breast
Was this pollisht stone beneath which she lyes prest
Stop, Reader, and Sigh while thou thinkst on the rest
With a just trim of Virtue her Soul was endu’d
Not affectedly Pious nor secretly lewd,
She cut even between the Cocquet and the Prude.”
—Matthew Prior (1664–1721)