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Volume 3, Issue 1
On Perturbation Bounds for the Joint Stationary Distribution of Multivariate Markov Chain Models

Wen Li, Lin Jiang, Wai-Ki Ching & Lu-Bin Cui

DOI: 10.4208/eajam.291112.090113a

East Asian J. Appl. Math., 3 (2013), pp. 1-17.

Published online: 2018-02

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  • Abstract

Multivariate Markov chain models have previously been proposed in for studying dependent multiple categorical data sequences. For a given multivariate Markov chain model, an important problem is to study its joint stationary distribution. In this paper, we use two techniques to present some perturbation bounds for the joint stationary distribution vector of a multivariate Markov chain with s categorical sequences. Numerical examples demonstrate the stability of the model and the effectiveness of our perturbation bounds.

  • Keywords

Multivariate Markov chain models, stationary distribution vector, condition number, relative bound.

  • AMS Subject Headings

65M10, 78A48

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{EAJAM-3-1, author = {Wen Li, Lin Jiang, Wai-Ki Ching and Lu-Bin Cui}, title = {On Perturbation Bounds for the Joint Stationary Distribution of Multivariate Markov Chain Models}, journal = {East Asian Journal on Applied Mathematics}, year = {2018}, volume = {3}, number = {1}, pages = {1--17}, abstract = {

Multivariate Markov chain models have previously been proposed in for studying dependent multiple categorical data sequences. For a given multivariate Markov chain model, an important problem is to study its joint stationary distribution. In this paper, we use two techniques to present some perturbation bounds for the joint stationary distribution vector of a multivariate Markov chain with s categorical sequences. Numerical examples demonstrate the stability of the model and the effectiveness of our perturbation bounds.

}, issn = {2079-7370}, doi = {https://doi.org/10.4208/eajam.291112.090113a}, url = {http://global-sci.org/intro/article_detail/eajam/10832.html} }
TY - JOUR T1 - On Perturbation Bounds for the Joint Stationary Distribution of Multivariate Markov Chain Models AU - Wen Li, Lin Jiang, Wai-Ki Ching & Lu-Bin Cui JO - East Asian Journal on Applied Mathematics VL - 1 SP - 1 EP - 17 PY - 2018 DA - 2018/02 SN - 3 DO - http://doi.org/10.4208/eajam.291112.090113a UR - https://global-sci.org/intro/article_detail/eajam/10832.html KW - Multivariate Markov chain models, stationary distribution vector, condition number, relative bound. AB -

Multivariate Markov chain models have previously been proposed in for studying dependent multiple categorical data sequences. For a given multivariate Markov chain model, an important problem is to study its joint stationary distribution. In this paper, we use two techniques to present some perturbation bounds for the joint stationary distribution vector of a multivariate Markov chain with s categorical sequences. Numerical examples demonstrate the stability of the model and the effectiveness of our perturbation bounds.

Wen Li, Lin Jiang, Wai-Ki Ching and Lu-Bin Cui. (2018). On Perturbation Bounds for the Joint Stationary Distribution of Multivariate Markov Chain Models. East Asian Journal on Applied Mathematics. 3 (1). 1-17. doi:10.4208/eajam.291112.090113a
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