In this work we extend the results developed in 2022 for a sequential change detection algorithm making use of Page's CUSUM statistic, the empirical distribution as an estimate of the pre-change distribution, and a universal code as a tool for estimating the post-change distribution, from the i.i.d. case to the Markov setup.

翻译：本研究将2022年提出的序贯变点检测算法结果从独立同分布情形扩展至马尔可夫框架。该算法采用Page的CUSUM统计量，以经验分布作为变化前分布的估计，并利用通用编码作为估计变化后分布的工具。