Maximum Probability and Relative Entropy Maximization. Bayesian Maximum Probability and Empirical Likelihood
Statistics Theory
2008-04-25 v1 Probability
Methodology
Statistics Theory
Abstract
Works, briefly surveyed here, are concerned with two basic methods: Maximum Probability and Bayesian Maximum Probability; as well as with their asymptotic instances: Relative Entropy Maximization and Maximum Non-parametric Likelihood. Parametric and empirical extensions of the latter methods - Empirical Maximum Maximum Entropy and Empirical Likelihood - are also mentioned. The methods are viewed as tools for solving certain ill-posed inverse problems, called Pi-problem, Phi-problem, respectively. Within the two classes of problems, probabilistic justification and interpretation of the respective methods are discussed.
Cite
@article{arxiv.0804.3926,
title = {Maximum Probability and Relative Entropy Maximization. Bayesian Maximum Probability and Empirical Likelihood},
author = {M. Grendar},
journal= {arXiv preprint arXiv:0804.3926},
year = {2008}
}
Comments
Intnl. Workshop on Applied Probability 2008, Compiegne, France