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THE SHANNON–MCMILLAN THEOREM FOR MARKOV CHAINS INDEXED BY A CAYLEY TREE IN RANDOM ENVIRONMENT

Published online by Cambridge University Press:  29 December 2017

Zhiyan Shi
Affiliation:
Faculty of Science, Jiangsu University, Zhenjiang 212013, China E-mail: shizhiyan1984@126.com
Pingping Zhong
Affiliation:
Faculty of Science, Jiangsu University, Zhenjiang 212013, China and Jingjiang College of Jiangsu University, Zhenjiang 212013, China E-mail: zpp2008@126.com
Yan Fan
Affiliation:
Jingjiang College of Jiangsu University, Zhenjiang 212013, China E-mail: free1002@126.com

Abstract

In this paper, we give the definition of tree-indexed Markov chains in random environment with countable state space, and then study the realization of Markov chain indexed by a tree in random environment. Finally, we prove the strong law of large numbers and Shannon–McMillan theorem for Markov chains indexed by a Cayley tree in a Markovian environment with countable state space.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2017 

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References

1.Benjamini, I. & Peres, Y. (1994). Markov chains indexed by trees. Annals of Probability 22: 219243.Google Scholar
2.Chen, X.X, Yang, W.G., & Wang, B. (2012). The equivalent definition of T-indexed Markov chains. Journal of Mathematical Study 45(4): 411414 (In Chinese).Google Scholar
3.Berger, T. & Ye, Z. (1990). Entropic aspects of random fields on trees. IEEE Transaction on Information Theory 36: 10061018.Google Scholar
4.Ye, Z. & Berger, T. (1996). Ergodic, regulary and asymptotic equipartition property of random fields on trees. Journal of Combinatorics, Information and System Sciences 21: 157184.Google Scholar
5.Ye, Z. & Berger, T. (1998). Information Measures for Discrete Random Fields. Beijing: Science Press.Google Scholar
6.Pemantle, R. (1992). Antomorphism invariant measure on trees. Annals of Probability 20: 15491566.Google Scholar
7.Yang, W.G. & Liu, W. (2000). Strong law of large numbers for Markov chains fields on a Bethe tree. Statistical and Probability Letters 49: 245250.Google Scholar
8.Yang, W.G. (2003). Some limit properties for Markov chains indexed by a homogeneous tree. Statistical and Probability Letters 65: 241250.Google Scholar
9.Yang, W.G. & Ye, Z. (2007). The asymptotic equipartition property for Markov chains indexed by a Homogeneous tree. IEEE Transaction on Information Theory 53(9): 32753280.Google Scholar
10.Huang, H.L. & Yang, W.G. (2008). Strong law of large number for Markov chains indexed by an infinite tree with uniformly bounded degree. Science in China 51(2): 195202.Google Scholar
11.Wang, B, Yang, W.G., & Shi, Z.Y. (2012). Strong laws of large numbers for countable Markov chains indexed by a Cayley tree. Science in China 42(10): 10311036.Google Scholar
12.Peng, W.C., Yang, W.G., & Shi, Z.Y. (2015). Strong law of large numbers for Markov chains indexed by spherically symmetric trees. Probability in the Engineering and Informational Sciences 29: 473481.Google Scholar
13.Dang, H., Yang, W.G., & Shi, Z.Y. (2015). The strong law of large numbers and the entropy ergodic theorem for nonhomogeneous bifurcating Markov chains indexed by a binary tree. IEEE Transaction on Information Theory 61(4): 16401648.Google Scholar
14.Nawrotzki, K. (1981). Discrete open system or Markov chains in a random environment, I. Journal of Information Process, Cybernet 17: 569599.Google Scholar
15.Nawrotzki, K. (1982). Discrete open system or Markov chains in a random environment, II. Journal of Information Process, Cybernet 18: 8398.Google Scholar
16.Cogburn, R. (1991). On the central limit theorem for Markov chains in random environments. Annals of Probability 19(2): 587604.Google Scholar
17.Cogburn, R. (1984). The ergodic theory of Markov chains in random environments. Z Wahrsch Verw Gebiete 66: 109128.Google Scholar
18.Cogburn, R. (1990). On direct convergence and periodicity for transition probabilities of Markov chains in random environments. Annals of Probability 18(2): 642654.Google Scholar
19.Hu, D.H. (2004). The construction of Markov processes in random environments and the equivalence theorems. Science in China (Series A) 47: 481496.Google Scholar
20.Hu, D.H. (2004). The existence and uniqueness of q-processes in random environments. Science in China (Series A) 47: 641658.Google Scholar
21.Li, Y.Q. (2003). Instantaneous and invariant functions for Markov chains in double infinite environment. Annals of Mathematics 24(2): 515520 (In Chinese).Google Scholar
22.Li, Y.Q., Wang, S.M., & Hu, Y.L. (2008). A class of strong limit theorems for Markov chains in Markov environments. Progress in Mathematics 37(5): 543550 (In Chinese).Google Scholar
23.Shi, Z.Y. & Yang, W.G. (2017). The definition of tree-indexed Markov chains in random environment and their existence. Communications in Statistics – Theory and Methods 46(16): 79347941.Google Scholar
24.Guyon, J. (2007). Limit theorems for bifurcating Markov chains. Application to the detection of cellular aging. Annals of Applied Probability 17(5–6): 15381569.Google Scholar
25.Huang, H.L. (2017). The asymptotic behavior for Markov chains in a finite i.i.d random environment indexed by Cayley trees. Filomat 31(2): 273283.Google Scholar
26.Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal 27: 379–423, 623656.Google Scholar
27.McMillan, B. (1953). The basic theorems of information theory. Annals of Mathematical Statistics 24: 196219.Google Scholar
28.Breiman, L. (1957). The individual ergodic theorem of information theory. Annals of Mathematical Statistics 31: 809810.Google Scholar
29.Chung, K.L. (1961). The ergodic theorem of information theory. Annals of Mathematical Statistics 32: 612614.Google Scholar
30.Barron, A.R. (1985). The strong ergodic theorem for densities: generalized Shannon–Mcmillan–Breiman theorem. Annals of Probability 13: 12921303.Google Scholar
31.Algoet, P.H. & Cover, T.M. (1988). A sandwich proof of the Shannon–Mcmillan–Breiman theorem. Annals of Probability 16(2): 899909.Google Scholar
32.Liu, W. & Yang, W.G. (1996). A extension of Shannon-Mcmillan theorem and some limit properties for nonhomogeneous Markov chains. Stochastic Processes and their Applications 61: 129145.Google Scholar
33.Yang, W.G. & Liu, W. (2004). The asymptotic equipartition property for mth-order nonhomogeneous Markov information sources. IEEE Transactions on Information Theory 50(12): 33263330.Google Scholar