Information theoretic methods in parameter estimation

Abstract

In the present communication entropy optimization principles namely maximum entropy principle and minimum cross entropy principle are defined and a critical approach of parameter estimation methods using entropy optimization methods is described in brief. Maximum entropy principle and its applications in deriving other known methods in parameter estimation are discussed. The relation between maximum likelihood estimation and maximum entropy principle has been derived. The relation between minimum divergence information principle and other classical method minimum Chi-square is studied. A comparative study of Fisher’s measure of information and minimum divergence measure is made. Equivalence of classical parameter estimation methods and information theoretic methods is studied. An application for estimation of parameter estimation when interval proportions are given is discussed with a numerical example. Key words: Parameter estimation, maximum entropy principle, minimum divergence measure, maximum likelihood estimation, minimum interdependence principle.