Selection of the Most Probable Best under Input Uncertainty

Kyoung Kuk Kim, Taeho Kim, Eunhye Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


We consider a ranking and selection problem whose configuration depends on a common input model estimated from finite real-world observations. To find a solution robust to estimation error in the input model, we introduce a new concept of robust optimality: the most probable best. Taking the Bayesian view, the most probable best is defined as the solution whose posterior probability of being the best is the largest given the real-world data. Focusing on the case where the posterior on the input model has finite support, we study the large deviation rate of the probability of incorrectly selecting the most probable best and formulate an optimal computing budget allocation (OCBA) scheme for this problem. We further approximate the OCBA problem to obtain a simple and interpretable budget allocation rule and propose sequential learning algorithms. A numerical study demonstrates good performances of the proposed algorithms.

Original languageEnglish (US)
Title of host publication2021 Winter Simulation Conference, WSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665433112
StatePublished - 2021
Event2021 Winter Simulation Conference, WSC 2021 - Phoenix, United States
Duration: Dec 12 2021Dec 15 2021

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2021 Winter Simulation Conference, WSC 2021
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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