Bounds on Mixing Time for Time-Inhomogeneous Markov Chains

Mixing of finite time-homogeneous Markov chains is well understood nowadays, with a rich set of techniques to estimate their mixing time. In this paper, we study the mixing time of random walks in dynamic random environments. To that end, we propose a concept of mixing time for time-inhomogeneous Markov chains. We then develop techniques to estimate this mixing time by extending the evolving set method of Morris and Peres (2005). We apply these techniques to study a random walk on a dynamic Erd & odblac;s-R & eacute;nyi graph, proving that our proposed definition of mixing time is O(log(n)) when the graph is well above the connectivity threshold. We also give an almost matching lower bound.