Bayesian Quickest Detection of Changes in Statistically Periodic Processes
Bayesian Quickest Detection of Changes in Statistically Periodic Processes
复制标题
贝叶斯最快检测统计周期性过程的变化
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Gene T. Whipps
中科院分区：
文献类型：
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作者：
T. Banerjee;Prudhvi K. Gurram;Gene T. Whipps
贝叶斯最优性理论是为一类称为独立和周期同分布（i.p.i.d.）的随机过程中的最快变化检测而开发的。流程.这类过程可用于对周期性变化的统计行为进行建模。提出了一种算法，称为E-Shiryaev算法，并示出渐近最小化的平均检测延迟的虚警概率的约束。它还表明，该算法的统计可以计算递归和使用有限的内存量。这个问题在网络物理系统和生物学中的异常检测问题中有应用，其中已经观察到周期性的统计行为。
Bayesian optimality theory is developed for quickest change detection in a class of stochastic processes called independent and periodically identically distributed (i.p.i.d.) processes. This class of processes can be used to model periodically varying statistical behavior. An algorithm called the periodic-Shiryaev algorithm is proposed and is shown to asymptotically minimize the average detection delay subject to a constraint on the probability of false alarm. It is also shown that the statistic for this algorithm can be computed recursively and using a finite amount of memory. This problem has applications in anomaly detection problems in cyber-physical systems and biology, where periodic statistical behavior has been observed.