异常检验：主动策略与非渐近分析
Testing for Anomalies: Active Strategies and Non-asymptotic Analysis
摘要：
验证多部件系统是否有异常或没有的问题得到解决。可以以数据驱动的方式随时间探测每个分量，以获得指示所选分量是否异常的噪声观测。目的是最小化错误地声明系统没有异常的概率，同时确保正确声明系统安全的概率足够大。这个问题被建模为一个积极的假设检验问题的内曼皮尔逊设置。在非渐近机制下设计并分析了随机选择和推理策略。对于一类特定的齐次问题，更强（相对于以前的工作）的非渐近逆和可扩展性的界限。
英文摘要：
The problem of verifying whether a multi-component system has anomalies or not is addressed. Each component can be probed over time in a data-driven manner to obtain noisy observations that indicate whether the selected component is anomalous or not. The aim is to minimize the probability of incorrectly declaring the system to be free of anomalies while ensuring that the probability of correctly declaring it to be safe is sufficiently large. This problem is modeled as an active hypothesis testing problem in the Neyman-Pearson setting. Component-selection and inference strategies are designed and analyzed in the non-asymptotic regime. For a specific class of homogeneous problems, stronger (with respect to prior work) non-asymptotic converse and achievability bounds are provided.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
