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

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 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. doi:10.1017/S1446181108000114