Quickest Linear Search over Correlated Sequences
Quickest Linear Search over Correlated Sequences
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DOI：
10.1109/tit.2016.2593772
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发表时间：
2016-10-01
影响因子：
2.5
通讯作者：
Poor, H. Vincent
中科院分区：
文献类型：
--
作者：
Heydari, Javad;Tajer, Ali;Poor, H. Vincent
考虑一组随机序列，每个序列由两个已知分布F-0和F-1中的一个独立且分布相同的随机变量组成。不同序列的潜在分布是相互关联的，这是由产生这些序列的机制中固有的物理耦合引起的。目标是设计最快的数据自适应和顺序搜索程序，以识别根据F-1生成的一个序列。最优设计包括在做出决定的平均延迟和误报率之间取得平衡，这是两个相反的价值数字。导出了最优决策规则和渐近最优决策规则，它们可以根据相关结构的不同采取完全不同的形式。提供了性能和采样复杂性分析来描述决策延迟和质量之间的权衡。同时研究了多序列并行采样的推广问题。
Consider a set of random sequences, each consisting of independent and identically distributed random variables drawn from one of the two known distributions F-0 and F-1. The underlying distributions of different sequences are correlated, induced by an inherent physical coupling in the mechanisms generating these sequences. The objective is to design the quickest data-adaptive and sequential search procedure for identifying one sequence generated according to F-1. The optimal design involves striking a balance between the average delay in reaching a decision and the rate of false alarms, as two opposing figures of merit. Optimal and asymptotically optimal decision rules are derived, which can take radically different forms depending on the correlation structure. Performance and sampling complexity analyses are provided to delineate the tradeoff between decision delay and quality. The generalization to parallel sampling, in which multiple sequences are sampled at the same time, is also investigated.