Online Supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate’
| Data(s) |
14/07/2016
14/07/2016
2016
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| Resumo |
This document is the Online Supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate,’ to be published in the IEEE Transactions of Automatic Control in 2017. In this online appendix, we test the performance of the AOMAP (asymptotically optimal myopic allocation policy) algorithm under the unknown variances scenario and compare it with EI (expected improvement) and OCBA (optimal computing budget allocation). This work was supported in part by the National Science Foundation (NSF) under Grants CMMI-1362303 and CMMI-1434419, by the Air Force of Scientific Research (AFOSR) under Grant FA9550-15-10050, by the National Natural Science Foundation of China (Project 11171256), and by the China Postdoctoral Science Foundation under Grant 2015M571495. |
| Identificador |
doi:10.13016/M2S20G |
| Relação |
Robert H. Smith School of Business Decision & Information Technologies Digital Repository at the University of Maryland University of Maryland (College Park, MD) |
| Palavras-Chave | #statistical ranking and selection #Bayesian framework #asymptotic sampling ratio #optimal computing budget allocation #expected improvement #expected value of information #knowledge gradient #myopic allocation policy |
| Tipo |
Other |
