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THE ASYMPTOTIC EQUIPARTITION PROPERTY FOR ASYMPTOTIC CIRCULAR MARKOV CHAINS

Published online by Cambridge University Press:  18 March 2010

Pingping Zhong
Affiliation:
Faculty of Science, Jiangsu University, Zhenjiang, 212013, China E-mail: [email protected]
Weiguo Yang
Affiliation:
Faculty of Science, Jiangsu University, Zhenjiang, 212013, China E-mail: [email protected]
Peipei Liang
Affiliation:
Faculty of Science, Jiangsu University, Zhenjiang, 212013, China E-mail: [email protected]

Abstract

In this article, we study the asymptotic equipartition property (AEP) for asymptotic circular Markov chains. First, the definition of an asymptotic circular Markov chain is introduced. Then by applying the limit property for the bivariate functions of nonhomogeneous Markov chains, the strong limit theorem on the frequencies of occurrence of states for asymptotic circular Markov chains is established. Next, the strong law of large numbers on the frequencies of occurrence of states for asymptotic circular Markov chains is obtained. Finally, we prove the AEP for asymptotic circular Markov chains.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2010

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References

1.Barron, A.K. (1985). The strong ergodic theorem for densities: Generalized Shannon–McMillan–Breiman theorem. Annals of Probability 13: 12921303.CrossRefGoogle Scholar
2.Bowerman, B., David, H.T. & Isaacson, D. (1977). The convergence of Cesaro averages for certain nonstationary Markov chains. Stochastic Processes and their Applications 5: 221230.CrossRefGoogle Scholar
3.Breiman, L. (1957). The individual ergodic theorem of information theory. Annals of Mathematical Statistics 28: 629635.CrossRefGoogle Scholar
4.Chung, K.L. (1961). The ergodic theorem of information theory. Annals of Mathematical Statistics 32: 612614.CrossRefGoogle Scholar
5.Kieffer, J.C. (1974). A simple proof of the Moy–Perez generalization of the Shannon–McMillan theorem. Pacific Journal of Mathematics 51: 203204.CrossRefGoogle Scholar
6.McMillan, B. (1953). The basic theorems of information theory. Annals of Mathematical Statistics 24: 196219.CrossRefGoogle Scholar
7.Shannon, C. (1948). A mathematical theory of communication. Bell System Technical Journal 27: 379423.CrossRefGoogle Scholar
8.Yang, W.G. (1998). The asymptotic equipartition property for a nonhomogeneous markov Information source. Probability in the Engineering and Informational Sciences 21: 6166.Google Scholar
9.Yang, W.G. (2002). Convergence in the Cesàro sense and strong law of large numbers for nonhomogeneous Markov chains. Linear Algebra and its Applications 354: 275286.CrossRefGoogle Scholar
10.Yang, W.G. (2009). Strong law of large numbers for countable nonhomogeneous Markov chains. Linear Algebra Applications 430: 30083018.CrossRefGoogle Scholar
11.Zach, D. & Sunder, S. (2005). Large deviations for a class of nonhomogeneous Markov chains. Annals of Probability 15, 421486.Google Scholar