Bayesian Sequential Testing with Expectation Constraints

Stefan Ankirchner (1) , Maike Klein (2)

1 Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany]
2 TU Wien - Vienna University of Technology = Technische Universität Wien

We study a stopping problem arising from a sequential testing of two simple hypotheses H0 and H1 on the drift rate of a Brownian motion. We impose an expectation constraint on the stopping rules allowed and show that an optimal stopping rule satisfying the constraint can be found among the rules of the following type: stop if the posterior probability for H1 attains a given lower or upper barrier; or stop if the posterior probability comes back to a fixed intermediate point after a sufficiently large excursion. Stopping at the intermediate point means that the testing is abandoned without accepting H0 or H1. In contrast to the unconstrained case, optimal stopping rules, in general, cannot be found among interval exit times. Thus, optimal stopping rules in the constrained case qualitatively differ from optimal rules in the unconstrained case.

Mots clés: Bayesian sequential testing; optimal stopping; expectation constraint

Domaines: Mathématiques [math]; Probabilités [math.PR]; Statistiques [math.ST]

Stefan Ankirchner, Maike Klein. Bayesian Sequential Testing with Expectation Constraints. 2018. ⟨hal-01908132⟩