  We consider whether ergodic Markov chains with bounded step size remain
bounded in probability when their transitions are modified by an adversary on a
bounded subset. We provide counterexamples to show that the answer is no in
general, and prove theorems to show that the answer is yes under various
additional assumptions. We then use our results to prove convergence of various
adaptive Markov chain Monte Carlo algorithms.
