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.

翻译：在本文中,我们开发了一种新的变化检测算法,以探测在变化后内核未知的公制空间上Markov内核的变化。假设改变前和变化后内核是统一的异性,我们得出了中值延迟的上限和误警报之间中值间隔的下限。提供了数字模拟以证明我们的方法的有效性。