Sequential Change Detection Under Markov Setup With Unknown Prechange And Postchange Distributions

TL;DR

This paper extends sequential change detection methods using Page's CUSUM, empirical distributions, and universal coding from i.i.d. data to Markov processes, addressing more complex dependencies.

Contribution

It introduces a novel approach for change detection in Markov processes, adapting existing i.i.d. methods with universal coding techniques.

Findings

Effective detection in Markov setups

Generalizes previous i.i.d. results

Demonstrates robustness of the method

Abstract

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.