Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control.

ABSTRACT: In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments. In this paper, we propose to incorporate multi-armed bandit approaches into sequential change-point detection to develop an efficient bandit change-point detection algorithm based on the limiting Bayesian approach to incorporate a prior knowledge of potential changes. Our proposed algorithm, termed Thompson-Sampling-Shiryaev-Roberts-Pollak (TSSRP), consists of two policies per time step: the adaptive sampling policy applies the Thompson

SUBMITTER: Zhang W 

PROVIDER: S-EPMC10027391 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature