非平稳过程中的鲁棒最快变化检测
Robust Quickest Change Detection in Non-Stationary Processes
AI总结：
针对变化后分布随时间变化且信息不精确的非平稳过程，本文利用最不利分布构建鲁棒且最优的最快变化检测算法，并通过真实与模拟数据验证其有效性。
摘要：
本文开发了用于鲁棒检测非平稳过程中变化的最优算法。所谓非平稳过程，是指变化后的数据分布会随时间变化的过程。决策者无法获得关于变化后分布的精确信息。研究表明，如果变化后的非平稳分布族中存在一个在明确定义意义下最不利的分布，那么利用该最不利分布设计的算法既是鲁棒的，也是最优的。非平稳过程常见于公共卫生监测以及航天和军事应用中。本文将所提出的鲁棒算法应用于真实数据和模拟数据，以证明其有效性。
英文摘要：
Optimal algorithms are developed for robust detection of changes in non-stationary processes. These are processes in which the distribution of the data after change varies with time. The decision-maker does not have access to precise information on the post-change distribution. It is shown that if the post-change non-stationary family has a distribution that is least favorable in a well-defined sense, then the algorithms designed using the least favorable distributions are robust and optimal. Non-stationary processes are encountered in public health monitoring and space and military applications. The robust algorithms are applied to real and simulated data to show their effectiveness.
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。
