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COMPUTABLE STRONGLY ERGODIC RATES OF CONVERGENCE FOR CONTINUOUS-TIME MARKOV CHAINS

  • YUANYUAN LIU (a1), HANJUN ZHANG (a2) and YIQIANG ZHAO (a3)

Abstract

In this paper, we investigate computable lower bounds for the best strongly ergodic rate of convergence of the transient probability distribution to the stationary distribution for stochastically monotone continuous-time Markov chains and reversible continuous-time Markov chains, using a drift function and the expectation of the first hitting time on some state. We apply these results to birth–death processes, branching processes and population processes.

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Copyright

Corresponding author

For correspondence; e-mail: liuyy@csu.edu.cn

References

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COMPUTABLE STRONGLY ERGODIC RATES OF CONVERGENCE FOR CONTINUOUS-TIME MARKOV CHAINS

  • YUANYUAN LIU (a1), HANJUN ZHANG (a2) and YIQIANG ZHAO (a3)

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