Multi-dimensional sequential testing and detection

Abstract

We study extensions to higher dimensions of the classical Bayesian sequential testing and detection problems for Brownian motion. In the main result we show that, for a large class of problem formulations, the cost function is unilaterally concave. This concavity result is then used to deduce structural properties for the continuation and stopping regions in specific examples.

References in corpus (2)

• Adaptive Poisson disorder problem
• Multisource Bayesian sequential change detection

Cited by in corpus (2)

• On the continuity of optimal stopping surfaces for jump-diffusions
• Bayesian sequential composite hypothesis testing in discrete time