scholarly journals Stationary probability vectors of higher-order Markov chains

2015 ◽  
Vol 473 ◽  
pp. 114-125 ◽  
Author(s):  
Chi-Kwong Li ◽  
Shixiao Zhang
Author(s):  
Wai-Ki Ching ◽  
Ximin Huang ◽  
Michael K. Ng ◽  
Tak-Kuen Siu
Keyword(s):  

2008 ◽  
Vol 428 (2-3) ◽  
pp. 492-507 ◽  
Author(s):  
Wai-Ki Ching ◽  
Michael K. Ng ◽  
Eric S. Fung
Keyword(s):  

1992 ◽  
Vol 6 (3) ◽  
pp. 391-408 ◽  
Author(s):  
Eckhaard Platen

This paper proposes a method that allows the construction of discrete-state Markov chains approximating an Ito-diffusion process. The transition probabilities of the Markov chains are chosen in such a way that functionals converge with a desired weak order with respect to vanishing step size under sufficient smoothness assumptions.


2005 ◽  
Vol 37 (04) ◽  
pp. 1075-1093 ◽  
Author(s):  
Quan-Lin Li ◽  
Yiqiang Q. Zhao

In this paper, we consider the asymptotic behavior of stationary probability vectors of Markov chains of GI/G/1 type. The generating function of the stationary probability vector is explicitly expressed by theR-measure. This expression of the generating function is more convenient for the asymptotic analysis than those in the literature. TheRG-factorization of both the repeating row and the Wiener-Hopf equations for the boundary row are used to provide necessary spectral properties. The stationary probability vector of a Markov chain of GI/G/1 type is shown to be light tailed if the blocks of the repeating row and the blocks of the boundary row are light tailed. We derive two classes of explicit expression for the asymptotic behavior, the geometric tail, and the semigeometric tail, based on the repeating row, the boundary row, or the minimal positive solution of a crucial equation involved in the generating function, and discuss the singularity classes of the stationary probability vector.


1990 ◽  
Vol 27 (03) ◽  
pp. 521-529 ◽  
Author(s):  
Guy Louchard ◽  
Guy Latouche

We consider a finite Markov chain with nearly-completely decomposable stochastic matrix. We determine bounds for the error, when the stationary probability vector is approximated via a perturbation analysis.


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