A method devised by Jaynes selects these functions based on the principle of maximum entropy.
A method devised by Jaynes selects these functions based on the principle of maximum entropy. Three major classes of these functions are the density ratio class, the neighborhood class, and the conjugate class. When this function is chosen to be proportional to the square root of the determinant of the Fischer information matrix, it is known as a Jeffreys one of these. If this function is picked on the basis of representing no useful information, it is called objective and is usually improper, while subjective ones represent some independent knowledge of the probability distribution. Frequently criticized by frequentists, for ten points, identify this quantity which, when multiplied by the likelihood function gives the posterior probability, according to Bayes' theorem.
A method devised by Jaynes selects these functions based on the principle of maximum entropy.
prior probability distribution