{"id":"https://openalex.org/W4241861064","doi":"https://doi.org/10.1109/wsc.2014.7020216","title":"Parallel Bayesian policies for finite-horizon multiple comparisons with a known standard","display_name":"Parallel Bayesian policies for finite-horizon multiple comparisons with a known standard","publication_year":2014,"publication_date":"2014-12-01","ids":{"openalex":"https://openalex.org/W4241861064","doi":"https://doi.org/10.1109/wsc.2014.7020216"},"language":"en","primary_location":{"id":"doi:10.1109/wsc.2014.7020216","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2014.7020216","pdf_url":null,"source":{"id":"https://openalex.org/S4363608779","display_name":"Proceedings of the Winter Simulation Conference 2014","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Winter Simulation Conference 2014","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084113894","display_name":"Weici Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weici Hu","raw_affiliation_strings":["School of Operations Research & Information Engineering, Cornell University, Ithaca, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Operations Research & Information Engineering, Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039019367","display_name":"Peter I. Frazier","orcid":"https://orcid.org/0000-0002-3501-3341"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peter I. Frazier","raw_affiliation_strings":["School of Operations Research & Information Engineering, Cornell University, Ithaca, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Operations Research & Information Engineering, Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100337737","display_name":"Jing Xie","orcid":"https://orcid.org/0009-0000-7349-9250"},"institutions":[{"id":"https://openalex.org/I4210149506","display_name":"American Express (United States)","ror":"https://ror.org/05rckx884","country_code":"US","type":"company","lineage":["https://openalex.org/I4210149506"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Xie","raw_affiliation_strings":["American Express Company, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"American Express Company, New York, NY, USA","institution_ids":["https://openalex.org/I4210149506"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3904","last_page":"3915"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9337999820709229,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9337999820709229,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6625423431396484},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.6166208982467651},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5961011648178101},{"id":"https://openalex.org/keywords/thompson-sampling","display_name":"Thompson sampling","score":0.5194380283355713},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5150303840637207},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.4991474151611328},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.49396640062332153},{"id":"https://openalex.org/keywords/simplicity","display_name":"Simplicity","score":0.46980127692222595},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.4550013542175293},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4423756003379822},{"id":"https://openalex.org/keywords/bernoullis-principle","display_name":"Bernoulli's principle","score":0.438377320766449},{"id":"https://openalex.org/keywords/bellman-equation","display_name":"Bellman equation","score":0.43576669692993164},{"id":"https://openalex.org/keywords/finite-set","display_name":"Finite set","score":0.43532803654670715},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.41998499631881714},{"id":"https://openalex.org/keywords/time-horizon","display_name":"Time horizon","score":0.4156380295753479},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3528040945529938},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24569407105445862},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1459580659866333},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.13837963342666626},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10621875524520874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6625423431396484},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6166208982467651},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5961011648178101},{"id":"https://openalex.org/C73602740","wikidata":"https://www.wikidata.org/wiki/Q7795822","display_name":"Thompson sampling","level":3,"score":0.5194380283355713},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5150303840637207},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4991474151611328},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.49396640062332153},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.46980127692222595},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4550013542175293},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4423756003379822},{"id":"https://openalex.org/C152361515","wikidata":"https://www.wikidata.org/wiki/Q181328","display_name":"Bernoulli's principle","level":2,"score":0.438377320766449},{"id":"https://openalex.org/C14646407","wikidata":"https://www.wikidata.org/wiki/Q1430750","display_name":"Bellman equation","level":2,"score":0.43576669692993164},{"id":"https://openalex.org/C162392398","wikidata":"https://www.wikidata.org/wiki/Q272404","display_name":"Finite set","level":2,"score":0.43532803654670715},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.41998499631881714},{"id":"https://openalex.org/C28761237","wikidata":"https://www.wikidata.org/wiki/Q7805321","display_name":"Time horizon","level":2,"score":0.4156380295753479},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3528040945529938},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24569407105445862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1459580659866333},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.13837963342666626},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10621875524520874},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wsc.2014.7020216","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2014.7020216","pdf_url":null,"source":{"id":"https://openalex.org/S4363608779","display_name":"Proceedings of the Winter Simulation Conference 2014","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Winter Simulation Conference 2014","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1967310902","https://openalex.org/W1991604424","https://openalex.org/W1999676077","https://openalex.org/W2012757283","https://openalex.org/W2026507776","https://openalex.org/W2056921512","https://openalex.org/W2069209488","https://openalex.org/W2100987884","https://openalex.org/W2107563734","https://openalex.org/W2119516001","https://openalex.org/W2124550001","https://openalex.org/W2146928171","https://openalex.org/W2160860428","https://openalex.org/W2487144912","https://openalex.org/W2499002200","https://openalex.org/W2797328151","https://openalex.org/W3140571750","https://openalex.org/W3148687029","https://openalex.org/W4244276361","https://openalex.org/W4249200735","https://openalex.org/W6681455198","https://openalex.org/W6681952518"],"related_works":["https://openalex.org/W2166253248","https://openalex.org/W4286896030","https://openalex.org/W2032834442","https://openalex.org/W4252763756","https://openalex.org/W3034914593","https://openalex.org/W2613863488","https://openalex.org/W3153900688","https://openalex.org/W3095264310","https://openalex.org/W4296554090","https://openalex.org/W2887199102"],"abstract_inverted_index":{"We":[0,38,55],"consider":[1,56],"the":[2,97,100],"problem":[3,58],"of":[4,24,99],"multiple":[5],"comparisons":[6],"with":[7,78],"a":[8,21,35,51,60,67,79,91],"known":[9,36],"standard,":[10],"in":[11,59],"which":[12,29],"we":[13,48,73,89],"wish":[14],"to":[15,27,85],"allocate":[16],"simulation":[17,53],"effort":[18],"efficiently":[19],"across":[20],"finite":[22],"number":[23],"simulated":[25],"systems,":[26],"determine":[28],"systems":[30],"have":[31],"mean":[32],"performance":[33],"exceeding":[34],"threshold.":[37],"suppose":[39],"that":[40,47],"parallel":[41],"computing":[42],"resources":[43],"are":[44,49],"available,":[45],"and":[46,63,103],"given":[50],"fixed":[52],"budget.":[54],"this":[57],"Bayesian":[61],"setting,":[62],"formulate":[64],"it":[65],"as":[66],"stochastic":[68],"dynamic":[69],"program.":[70],"For":[71],"simplicity,":[72],"focus":[74],"on":[75,96],"Bernoulli":[76],"sampling,":[77],"linear":[80],"loss":[81],"function.":[82],"Using":[83],"links":[84],"restless":[86],"multi-armed":[87],"bandits,":[88],"provide":[90],"computationally":[92],"tractable":[93],"upper":[94,110],"bound":[95],"value":[98],"Bayes-optimal":[101],"policy,":[102],"an":[104],"index":[105],"policy":[106],"motivated":[107],"by":[108],"these":[109],"bounds.":[111]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
