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：本文扩展了2022年针对序贯变点检测算法所取得的研究成果。该算法利用Page的CUSUM统计量、作为变点前分布估计的经验分布，以及作为变点后分布估计工具的通用编码。我们将该算法从独立同分布情形推广至马尔可夫框架。