Multi-Stream Quickest Detection with Unknown Post-Change Parameters Under Sampling Control

The multi-stream quickest detection problem with unknown post-change parameters is studied under the sampling control constraint, where there are $M$ local processes in a system but one is only able to take observations from one of these $M$ local processes at each time instant. The objective is to raise a correct alarm as quickly as possible once the change occurs subject to both false alarm and sampling control constraints. We propose an efficient myopic-sampling-based quickest detection algorithm under sampling control constraint, and show it is asymptotically optimal in the sense of minimizing the detection delay under our context when the number $M$ of processes is fixed. Simulation studies are conducted to validate our theoretical results.