This paper considers the problem of sequentially detecting a change in the joint distribution of multiple data sources under a sampling constraint. Specifically, the channels or sources generate observations that are independent over time, but not necessarily independent at any given time instant. The sources follow an initial joint distribution, and at an unknown time instant, the joint distribution of an unknown subset of sources changes. Importantly, there is a hard constraint that only a fixed number of sources are allowed to be sampled at each time instant. The goal is to sequentially observe the sources according to the constraint, and stop sampling as quickly as possible after the change while controlling the false alarm rate below a user-specified level. The sources can be selected dynamically based on the already collected data, and thus, a policy for this problem consists of a joint sampling and change-detection rule. A non-randomized policy is studied, and an upper bound is established on its worst-case conditional expected detection delay with respect to both the change point and the observations from the affected sources before the change.

翻译：本文研究了在采样约束条件下，对多个数据源联合分布变化进行序贯检测的问题。具体而言，各通道或数据源在时间维度上生成独立观测值，但在同一时刻的观测值之间未必独立。这些数据源服从初始联合分布，且在某未知时刻，一个未知子集数据源的联合分布发生改变。关键约束条件为：每个时刻仅允许对固定数量的数据源进行采样。研究目标是：在约束条件下序贯观测数据源，在控制虚警率低于用户指定水平的前提下，尽可能快地检测到变化后停止采样。数据源可根据已收集数据动态选择，因此该问题的策略包含联合采样与变点检测规则。本文研究了一种非随机化策略，并建立了其最坏情况下关于变点位置及变化前受影响数据源观测值的条件期望检测延迟的上界。