Sequential Change Detection Under Markov Setup With Unknown Prechange And Postchange Distributions
arXiv:2510.2620444.4h-index: 12
AI Analysis
This work addresses change detection for sequential data in applications like monitoring, but it is incremental as it extends existing methods to a more general setting.
The authors tackled the problem of sequential change detection in Markov processes with unknown pre-change and post-change distributions, extending a 2022 algorithm from i.i.d. cases to the Markov setup.
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.