Quickest Detection with Discretely Controlled Observations
This work addresses the problem of quickest detection under observation constraints, which is relevant for applications where sensing resources are limited, but the results are theoretical and algorithmic without empirical validation on real-world benchmarks.
The paper studies a continuous-time Bayesian quickest detection problem where the agent is limited to a finite number of discrete observations and must adaptively decide when to observe to minimize detection delay and false alarm probability. The authors establish existence of optimal strategies, provide algorithms, and show convergence to the classical continuous observation problem as the number of observations tends to infinity.
We study a continuous time Bayesian quickest detection problem in which observation times are a scarce resource. The agent, limited to making a finite number of discrete observations, must adaptively decide his observation strategy to minimize detection delay and the probability of false alarm. Under two different models of observation rights, we establish the existence of optimal strategies, and formulate an algorithmic approach to the problem via jump operators. We describe algorithms for these problems, and illustrate them with some numerical results. As the number of observation rights tends to infinity, we also show convergence to the classical continuous observation problem of Shiryaev.