State learning and mixing in entropy of hidden Markov processes and the Gilbert-Elliott channel

DOI10.1109/18.746777zbMath0945.94010OpenAlexW2126785155MaRDI QIDQ4701247

Predrag R. Jelenković, Bertrand Hochwald

Publication date: 21 November 1999

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/18.746777

zbMATH Keywords

upper boundMarkov chainMarkov-modulated random walkfading channelBirkhoff contraction coefficientGilbert-Elliott channelfinite-past approximation

Mathematics Subject Classification ID

Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Channel models (including quantum) in information and communication theory (94A40)

Related Items (3)

Thermodynamics of the binary symmetric channel ⋮ Performance of the move-to-front algorithm with Markov-modulated request sequences ⋮ Exponential bounds for convergence of entropy rate approximations in hidden Markov models satisfying a path-mergeability condition

This page was built for publication: State learning and mixing in entropy of hidden Markov processes and the Gilbert-Elliott channel