2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/169193We give a characterization of Maximum Entropy/Minimum Relative Entropy inference by providing two `strong entropy concentration' theorems. These theorems unify and generalize Jaynes' `concentration phenomenon' and Van Campenhout and Cover's `conditional limit theorem'. The theorems characterize exactly in what sense a prior distribution Q conditioned on a given constraint, and the distribution P, minimizing the relative entropy D(P ||Q) over all distributions satisfying the constraint, are `close' to each other. We then apply our theorems to establish the relationship between entropy concentration and a game-theoretic characterization of Maximum Entropy Inference due to Topsoe and others.A somewhat modified version of this paper was published in Statistica Neerlandica 62(3), pages 374-392, 2008Information TheoryMachine LearningStatistics TheoryMethodologytext