Online Supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate’

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dc.contributor.authorPeng, Yijie
dc.contributor.authorFu, Michael
dc.date.accessioned2016-07-14T11:41:13Z
dc.date.available2016-07-14T11:41:13Z
dc.date.issued2016
dc.descriptionThis 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.en_US
dc.description.abstractIn 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).en_US
dc.description.sponsorshipThis 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.en_US
dc.identifierhttps://doi.org/10.13016/M2S20G
dc.identifier.urihttp://hdl.handle.net/1903/18490
dc.relation.isAvailableAtRobert H. Smith School of Businessen_us
dc.relation.isAvailableAtDecision & Information Technologiesen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectstatistical ranking and selectionen_US
dc.subjectBayesian frameworken_US
dc.subjectasymptotic sampling ratioen_US
dc.subjectoptimal computing budget allocationen_US
dc.subjectexpected improvementen_US
dc.subjectexpected value of informationen_US
dc.subjectknowledge gradienten_US
dc.subjectmyopic allocation policyen_US
dc.titleOnline Supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate’en_US
dc.typeOtheren_US

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online appendix supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate’
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