@inproceedings{e4bd858bc3564554968c6c0016fb2698,
title = "Estimation of entropy rate and R{\'e}nyi entropy rate for Markov chains",
abstract = "Estimation of the entropy rate of a stochastic process with unknown statistics, from a single sample path is a classical problem in information theory. While universal estimators for general families of processes exist, the estimates have not been accompanied by guarantees for fixed-length sample paths. We provide finite sample bounds on the convergence of a plug-in type estimator for the entropy rate of a Markov chain in terms of its alphabet size and its mixing properties. We also discuss R{\'e}nyi entropy rate estimation for reversible Markov chains.",
author = "Sudeep Kamath and Sergio Verdu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Symposium on Information Theory, ISIT 2016 ; Conference date: 10-07-2016 Through 15-07-2016",
year = "2016",
month = aug,
day = "10",
doi = "10.1109/ISIT.2016.7541386",
language = "English (US)",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "685--689",
booktitle = "Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory",
address = "United States",
}