论文标题
在采样控制下，用于实时监控的高维数据的匪徒更改点检测
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control
论文作者
论文摘要
在实时监视高维流数据的许多现实世界中，人们希望在发生一个不希望的事件或一旦发生，但是在采样控制限制下，在某种意义上，在资源构成环境中，每个人可能只能观察或使用所选组件数据进行决策时间制定。在本文中，我们建议将多军匪徒方法纳入顺序更改点检测中，以开发有效的匪徒更换点检测算法，该算法基于极限的贝叶斯方法，以结合对潜在变化的先验知识。我们提出的算法称为汤普森 - 塞尔亚耶耶夫·罗伯茨 - 波拉克（TSSRP），由每个时间步骤组成的两个策略组成：适应性抽样政策：汤普森采样算法适用于在探索中平衡探索的长期知识和统计范围的统计学范围，以平衡统计范围的统一范围，以确定统计学范围，以确定统计性奖励范围，以确定统计学范围的范围，以确定统计学的范围。通过总和收缩技术提高全球警报。广泛的数值模拟和案例研究证明了我们提出的TSSRP算法的统计和计算效率。
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 Sampling algorithm to balance between exploration for acquiring long-term knowledge and exploitation for immediate reward gain, and the statistical decision policy fuses the local Shiryaev-Roberts-Pollak statistics to determine whether to raise a global alarm by sum shrinkage techniques. Extensive numerical simulations and case studies demonstrate the statistical and computational efficiency of our proposed TSSRP algorithm.
