Convergence of some time inhomogeneous Markov chains via spectral techniques

We consider the problem of giving explicit spectral bounds for time inhomogeneous Markov chains on a finite state space. We give bounds that apply when there exists a probability ππ such that each of the different steps corresponds to a nice ergodic Markov kernel with stationary measure ππ. For instance, our results provide sharp bounds for models such as semi-random transpositions and semi-random insertions (in these cases ππ is the uniform probability on the symmetric group).