Maximum Probability and Relative Entropy Maximization. Bayesian Maximum Probability and Empirical Likelihood

Simple item page
dc.creatorGrendar, M.
dc.date2008-04-24
dc.date.accessioned2026-07-07T09:34:59Z
dc.date.available2026-07-07T09:34:59Z
dc.descriptionWorks, 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.
dc.descriptionIntnl. Workshop on Applied Probability 2008, Compiegne, France
dc.identifierhttps://arxiv.org/abs/0804.3926
dc.identifierhttp://arxiv.org/abs/0804.3926
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/159691
dc.subjectStatistics Theory
dc.subjectProbability
dc.subjectMethodology
dc.subject62F10; 62G10
dc.titleMaximum Probability and Relative Entropy Maximization. Bayesian Maximum Probability and Empirical Likelihood
dc.typetext

Files

Collections

arXiv