Detecting Changes in Hidden Markov Models
Statistics Theory
2019-01-29 v2 Applications
Statistics Theory
Abstract
We consider the problem of sequential detection of a change in the statistical behavior of a hidden Markov model. By adopting a worst-case analysis with respect to the time of change and by taking into account the data that can be accessed by the change-imposing mechanism we offer alternative formulations of the problem. For each formulation we derive the optimum Shewhart test that maximizes the worst-case detection probability while guaranteeing infrequent false alarms.
Cite
@article{arxiv.1901.08434,
title = {Detecting Changes in Hidden Markov Models},
author = {George V. Moustakides},
journal= {arXiv preprint arXiv:1901.08434},
year = {2019}
}