JeffreysPriors
Jeffreys priors are non-informative priors in Bayesian statistics named after Harold Jeffreys. They are designed to minimize subjective influence by using the data model itself, through the Fisher information. A key feature is invariance under reparameterization: inferences should not depend on how the parameter is expressed.
Mathematically, for a parameter vector θ, the Jeffreys prior is proportional to the square root of the
Common examples illustrate the idea. For a normal model with known variance, the Jeffreys prior for the
Practical considerations include that Jeffreys priors can be improper, potentially yielding improper posteriors if the data