Year: 2004
Type: conference-paper
Abstract: The method of Maximum (relative) Entropy (ME) is used to translate the information contained in the known form of the likelihood into a prior distribution for Bayesian inference. The argument is guided by intuition gained from the successful use of ME methods in statistical mechanics. For experiments that cannot be repeated the resulting “entropic prior” is formally identical with the Einstein fluctuation formula. For repeatable experiments, however, the expected value of the entropy of the likelihood turns out to be relevant information that must be included in the analysis. As an example the entropic prior for a Gaussian likelihood is calculated.
Source: AIP conference proceedings
DOI: 10.1063/1.1751380
Author: Ariel Caticha
Language: Lithuanian
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3037106936