Sequential detection of multiple change points in networks: a graphical model approach
Statistics Theory
2012-07-09 v1 Machine Learning
Statistics Theory
Abstract
We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for certain functionals of change points (minimum among a subset), and prove their asymptotic optimality properties in terms of expected detection delay time. Drawing from graphical model formalism, the sequential detection rules can be implemented by a computationally efficient message-passing protocol which may scale up linearly in network size and in waiting time. The effectiveness of our inference algorithm is demonstrated by simulations.
Cite
@article{arxiv.1207.1687,
title = {Sequential detection of multiple change points in networks: a graphical model approach},
author = {Arash Ali Amini and XuanLong Nguyen},
journal= {arXiv preprint arXiv:1207.1687},
year = {2012}
}