Limit Theorems for Markov Chains and Stochastic Properties of Dynamical Systems by Quasi-Compactness

2001 ◽  
Keyword(s):  
Dynamical Systems ◽  
Markov Chains ◽  
Limit Theorems ◽  
Stochastic Properties

Addendum: Limit Theorems for Harris Markov Chains. I

1992 ◽  
Vol 36 (2) ◽  
pp. 426-426
Author(s):  
V. K. Malinovskii
Keyword(s):  
Markov Chains ◽  
Limit Theorems

scholarly journals Limit Theorems for Semi-Markov Processes and Renewal Theory for Markov Chains

1978 ◽  
Vol 6 (5) ◽  
pp. 788-797 ◽  
Author(s):  
K. B. Athreya ◽  
D. McDonald ◽  
P. Ney
Keyword(s):  
Markov Chains ◽  
Markov Processes ◽  
Limit Theorems ◽  
Renewal Theory ◽  
Semi Markov Processes

scholarly journals A spectral approach for quenched limit theorems for random hyperbolic dynamical systems

2019 ◽  
Vol 373 (1) ◽  
pp. 629-664 ◽  
Author(s):  
D. Dragičević ◽  
G. Froyland ◽  
C. González-Tokman ◽  
S. Vaienti
Keyword(s):  
Dynamical Systems ◽  
Limit Theorems ◽  
Spectral Approach ◽  
Hyperbolic Dynamical Systems

SOME LIMIT THEOREMS OF DELAYED AVERAGES FOR COUNTABLE NONHOMOGENEOUS MARKOV CHAINS

2019 ◽  
pp. 1-19
Author(s):  
Pingping Zhong ◽  
Weiguo Yang ◽  
Zhiyan Shi ◽  
Yan Zhang
Keyword(s):  
Information Theory ◽  
Markov Chains ◽  
Limit Theorems ◽  
Law Of Large Numbers ◽  
Strong Law ◽  
Strong Ergodicity ◽  
Large Numbers ◽  
Nonhomogeneous Markov Chains ◽  
Definition Of ◽  
Bivariate Functions

AbstractThe purpose of this paper is to establish some limit theorems of delayed averages for countable nonhomogeneous Markov chains. The definition of the generalized C-strong ergodicity and the generalized uniformly C-strong ergodicity for countable nonhomogeneous Markov chains is introduced first. Then a theorem about the generalized C-strong ergodicity and the generalized uniformly C-strong ergodicity for the nonhomogeneous Markov chains is established, and its applications to the information theory are given. Finally, the strong law of large numbers of delayed averages of bivariate functions for countable nonhomogeneous Markov chains is proved.

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