Algorithms for finding steady state probabilities for some special classes of finite state Markov chains |
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| Affiliation: | 1. Research Institute of Engineering Technology, Hanyang University, Ansan 15588, Republic of Korea;2. Research & Development Center, Sooil Engineering Co., Ltd., Anyang 14057, Republic of Korea;3. Department of Civil and Environmental Engineering, Sejong University, Seoul 05006, Republic of Korea;4. Department of Civil and Environmental Engineering, Hanyang University, Ansan 15588, Republic of Korea;1. School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, PR China;2. Department of Computer Science, SZABIST Islamabad Campus, Pakistan;3. Department of Statistics, University of Sargodha, Pakistan;4. State Key Laboratory of Hydro-Science and Engineering and Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, PR China;5. Department of Statistics, Quaid-i-Azam University Islamabad, Pakistan;6. Statistics Department, COMSATS University, Islamabad, Pakistan;7. Faculty of Health Studies, University of Bradford, Bradford, BD7 1DP, UK;8. Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK;9. Department of Statistics, University of Wah, Wah Cantt, Pakistan;10. National Engineering Research Centre of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China;11. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China;12. Earth System and Global Change Lab, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, PR China;13. Department of Mathematics, City University of Science and Information Technology, Peshawar, Pakistan |
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| Abstract: | ![]()
Efficient algorithms for finding steady state probabilities are presented and compared with the Gaussian elimination method for two special classes of finite state Markov chains. One class has block matrix steps and a possible jump of up to k block steps, and the other is a generalization of the class considered by Shanthikumar and Sargent where each element is a matrix. |
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