On Efficient Estimates of the Rate of Convergence for Markov Chains

The paper presents efficient approaches for evaluating convergence rate in total variation for finite and general Markov chains. The motivation for studying convergence rate in this metric is its usefulness in various limit theorems (see, for example, [3]). For homogeneous Markov chains the goal is to compare several different methods: (1) the second eigenvalue for the transition matrix method (the method no. 1), (2) the method based on Markov – Dobrushin’s ergodic coefficient, and the new spectral method developed in [18,19], as well as modifications of they both by iterations (the “other methods”). We answer the question whether or not the “other methods” may provide the optimal or close to optimal convergence rate in the case of homogeneous Markov chains. The answer turns out to be positive for appropriate modifications of both “other methods”. The analogues of these “other methods” for the non-homogeneous Markov chains are also presented. The work is theoretical. However, the methods of computing efficient bounds of convergence rates may be in demand in various applied areas.