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In practice, simulation parameters are estimated from finite data, which introduces model uncertainty. In “Selection of the Most Probable Best,” Taeho Kim, Kyoung-Kuk Kim, and Eunhye Song propose a new estimator of the optimum, the most probable best (MPB), for ranking and selection (R&S) when the model uncertainty is characterized by a posterior distribution on the parameter given data. Defined as the solution with the largest posterior probability of optimality, selecting the MPB requires simulating at all parameter-solution combinations. The authors focus on when the posterior has a finite support and derive the convergence rate of the probability of correctly selecting the MPB as a function of sampling fractions of all solution-parameter pairs. They propose a lower bound on the rate function that allows easier characterization of the optimal sampling fractions and devise a sequential sampling algorithm from it. The algorithm shows superior empirical performance over benchmarks.
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Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models
Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. The advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.
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- PAR ID:
- 10506439
- Publisher / Repository:
- Taylor and Francis
- Date Published:
- Journal Name:
- Technometrics
- Volume:
- 66
- Issue:
- 2
- ISSN:
- 0040-1706
- Page Range / eLocation ID:
- 157 to 171
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1080/00401706.2023.2246157
