Computable strongly ergodic rates of convergence for continuous-time Markov chains. (English) Zbl 1152.60341
Summary: 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.
MSC:
| 60J27 | Continuous-time Markov processes on discrete state spaces |
| 60J80 | Branching processes (Galton-Watson, birth-and-death, etc.) |
Keywords:
strong ergodicity; convergence rate; birth and death process; branching process; population processReferences:
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