Selection of the Most Probable Best

arXiv:2207.07533v2 Announce Type: replace-cross
Abstract: We consider an expected-value ranking and selection (R&S) problem where all k solutions' simulation outputs depend on a common parameter whose uncertainty can be modeled by a distribution. We define the most probable best (MPB) to be the solution that has the largest probability of being optimal with respect to the distribution and design an efficient sequential sampling algorithm to learn the MPB when the parameter has a finite support. We derive the large deviations …

abstract arxiv cs.lg distribution probability ranking simulation solution solutions stat.me stat.ml type uncertainty value