The asymptotic equipartition property of Markov chains in single infinite Markovian environment on countable state space
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Publication:5087036
DOI10.1080/17442508.2019.1567730zbMath1495.60068OpenAlexW2908568591MaRDI QIDQ5087036
Baihui Wu, Yan Fan, Zhiyan Shi, Dan Bao
Publication date: 8 July 2022
Published in: Stochastics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/17442508.2019.1567730
Strong limit theorems (60F15) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Information theory (general) (94A15)
Related Items (2)
Some generalized strong limit theorems for Markov chains in bi-infinite random environments ⋮ A class of small deviation theorems for Markov chains in bi-infinite random environment
Cites Work
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- A Mathematical Theory of Communication
- On the central limit theorem for Markov chains in random environments
- On Markov chains in space-time random environments
- On direct convergence and periodicity for transition probabilities of Markov chains in random environments
- The strong ergodic theorem for densities: Generalized Shannon-McMillan- Breiman theorem
- A sandwich proof of the Shannon-McMillan-Breiman theorem
- An extension of Shannon-McMillan theorem and some limit properties for nonhomogeneous Markov chains
- A strong limit theorem for the average of ternary functions of Markov chains in bi-infinite random environments
- The Strong Law of Large Numbers and the Entropy Ergodic Theorem for Nonhomogeneous Bifurcating Markov Chains Indexed by a Binary Tree
- The ergodic theory of Markov chains in random environments
- The Individual Ergodic Theorem of Information Theory
- The Asymptotic Equipartition Property for<tex>$M$</tex>th-Order Nonhomogeneous Markov Information Sources
- Markov Chains and Stochastic Stability
- A Note on the Ergodic Theorem of Information Theory
- Probability
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