  In this paper, we develop a new change detection algorithm for detecting a
change in the Markov kernel over a metric space in which the post-change kernel
is unknown. Under the assumption that the pre- and post-change Markov kernel is
uniformly ergodic, we derive an upper bound on the mean delay and a lower bound
on the mean time between false alarms. A numerical simulation is provided to
demonstrate the effectiveness of our method.
