For complex high-dimensional target distributions, Markov chain Monte
Carlo methods often require significant expertise and tuning.
Adaptive MCMC methods address this by attempting to perform online
learn from the sample history. We describe some theoretical results on
mixing times of several adaptive strategies, and show that they can be
classified by their effect on the convergence behavior. We show that a
hybrid algorithm using two distinct methods of adaptation outperforms
either individually.