Posterior-based stopping rules for Bayesian ranking-and-selection procedures. Eckman, D. J. & Henderson, S. G. INFORMS Journal on Computing, 2021. To appear![Posterior-based stopping rules for Bayesian ranking-and-selection procedures [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper ![Posterior-based stopping rules for Bayesian ranking-and-selection procedures [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Sequential ranking-and-selection procedures deliver Bayesian guarantees by repeatedly evaluating a posterior quantity of interest - e.g., the posterior probability of correct selection or posterior expected opportunity cost - and terminating when this quantity crosses some threshold. We discuss the perceived shortcomings of the posterior probability of correct selection guarantee. Motivated by settings in which there are many alternatives, we develop several methods for improving the computational efficiency of exactly checking these kinds of posterior-based stopping rules. We demonstrate via simulation experiments that the proposed methods can, in some instances, significantly reduce the number of simulation replications taken compared to procedures that instead use cheaply computable bounds on the posterior quantity of interest, e.g., those based on Bonferroni's or Slepian's inequalities. We further show that the use of a Monte Carlo estimate of the posterior quantity of interest allows for these savings to be gained with little added computational effort.
@article{eckhen20b,
abstract = {Sequential ranking-and-selection procedures deliver Bayesian guarantees by repeatedly evaluating a posterior quantity of interest - e.g., the posterior probability of correct selection or posterior expected opportunity cost - and terminating when this quantity crosses some threshold. We discuss the perceived shortcomings of the posterior probability of correct selection guarantee. Motivated by settings in which there are many alternatives, we develop several methods for improving the computational efficiency of exactly checking these kinds of posterior-based stopping rules. We demonstrate via simulation experiments that the proposed methods can, in some instances, significantly reduce the number of simulation replications taken compared to procedures that instead use cheaply computable bounds on the posterior quantity of interest, e.g., those based on Bonferroni's or Slepian's inequalities. We further show that the use of a Monte Carlo estimate of the posterior quantity of interest allows for these savings to be gained with little added computational effort.},
author = {David J. Eckman and Shane G. Henderson},
date-added = {2018-05-04 15:29:19 +0000},
date-modified = {2022-05-20 10:43:50 -0400},
journal = {{INFORMS} Journal on Computing},
note = {To appear},
title = {Posterior-based stopping rules for {B}ayesian ranking-and-selection procedures},
url = {https://doi.org/10.1287/ijoc.2021.1132},
url_paper = {pubs/posterior_based_stopping.pdf},
year = {2021},
bdsk-url-1 = {https://doi.org/10.1287/ijoc.2021.1132}}
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