2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | S.11.1 | ||
| Paper Title | Rényi Divergence rates of Ergodic Markov Chains: existence, explicit expressions and properties | ||
| Authors | Valérie Girardin, Laboratoire de Mathématiques Nicolas Oresme, France; Philippe Regnault, Laboratoire de Mathématiques de Reims, France | ||
| Session | S.11: Renyi Entropy | ||
| Presentation | Lecture | ||
| Track | Shannon Theory | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | The Kullback-Leibler divergence rate of two finite or denumerable ergodic Markov chains is well-known to exist and have an explicit expression as a function of the transition matrices of the chains, allowing access to classical tools for applications, such as minimization under constraints or projections on convex sets. The existence of Rényi divergence rates of ergodic Markov chains has been established in Rached et al. (2001), Girardin and Lhote (2015); here we establish explicit expressions for them and prove some properties of the resulting measures of discrepancy between stochastic matrices, opening the way to applications. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
