2012/08/26 by Yong-Hua Mao, Yongming Tai, Mao, YongHua +5
Mathematics · Business, Management and Accounting · Decision Sciences · #Markov Chains and Monte Carlo Methods #Advanced Queuing Theory Analysis #Probability and Risk Models
paper · pdf · doi:10.48550/arxiv.1208.5225
Ergodicity is a fundamental issue for a stochastic process. In this paper, we refine results on ergodicity for a general type of Markov chain to a specific type or the GI/G/1-type Markov chain, which has many interesting and important applications in various areas. It is of interest to obtain conditions in terms of system parameters or the given information about the process, under which the chain has various ergodic properties. Specifically, we provide necessary and sufficient conditions for geometric, strong and polynomial ergodicity, respectively.