Computable strongly ergodic rates of convergence for continuous-time Markov chains

Yuanyuan Liu, Hanjun Zhang, Yiqiang Zhao


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 ones, 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.



Remember, for most actions you have to record/upload into this online system
and then inform the editor/author via clicking on an email icon or Completion button.
ANZIAM Journal, ISSN 1446-8735, copyright Australian Mathematical Society.