{"id":"https://openalex.org/W7160200397","doi":"https://doi.org/10.48550/arxiv.2605.00379","title":"Computationally Efficient Experimental Design with Generalized Posteriors","display_name":"Computationally Efficient Experimental Design with Generalized Posteriors","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160200397","doi":"https://doi.org/10.48550/arxiv.2605.00379"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2605.00379","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.00379","pdf_url":"https://arxiv.org/pdf/2605.00379","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2605.00379","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081609467","display_name":"Luke Hagar","orcid":"https://orcid.org/0000-0002-1093-9463","observed_orcids":["https://orcid.org/0000-0002-1093-9463"]},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hagar, Luke","raw_affiliation_strings":["Clinical Trials Capability , The University of Queensland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clinical Trials Capability , The University of Queensland","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024916657","display_name":"James M. McGree","orcid":"https://orcid.org/0000-0003-2997-8929","observed_orcids":["https://orcid.org/0000-0003-2997-8929"]},"institutions":[{"id":"https://openalex.org/I160993911","display_name":"Queensland University of Technology","ror":"https://ror.org/03pnv4752","country_code":"AU","type":"education","lineage":["https://openalex.org/I160993911"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"McGree, James M.","raw_affiliation_strings":["School of Mathematical Sciences , Queensland University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences , Queensland University of Technology","institution_ids":["https://openalex.org/I160993911"]}]}],"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":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.801800012588501,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.801800012588501,"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"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.10000000149011612,"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"}},{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.08560000360012054,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/model-misspecification","display_name":"model misspecification","score":0.8209999799728394},{"id":"https://openalex.org/keywords/sample-size-calculation","display_name":"sample size determination","score":0.8550000190734863},{"id":"https://openalex.org/keywords/operating-characteristics","display_name":"operating characteristics","score":0.6769999861717224},{"id":"https://openalex.org/keywords/bayesian-decision-procedure","display_name":"Bayesian decision procedure","score":0.7699999809265137},{"id":"https://openalex.org/keywords/adaptive-clinical-trials","display_name":"adaptive clinical trials","score":0.7960000038146973},{"id":"https://openalex.org/keywords/time-to-event-endpoints","display_name":"time-to-event outcomes","score":0.9919999837875366},{"id":"https://openalex.org/keywords/m-estimation","display_name":"M-estimation","score":0.7089999914169312},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.6110000014305115}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6366000175476074},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.6248000264167786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5742999911308289},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5636000037193298},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.5562000274658203},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5281000137329102},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4528999924659729},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3659000098705292},{"id":"https://openalex.org/C99173435","wikidata":"https://www.wikidata.org/wiki/Q4874469","display_name":"Bayesian experimental design","level":5,"score":0.3409000039100647},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3276999890804291},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3172999918460846},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3165999948978424},{"id":"https://openalex.org/C100279318","wikidata":"https://www.wikidata.org/wiki/Q467440","display_name":"Sample space","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3059000074863434},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.302700012922287},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27140000462532043},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.26919999718666077},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.258899986743927}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2605.00379","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.00379","pdf_url":"https://arxiv.org/pdf/2605.00379","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2605.00379","doi":"https://doi.org/10.48550/arxiv.2605.00379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00379","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2605.00379","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.00379","pdf_url":"https://arxiv.org/pdf/2605.00379","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320984","display_name":"University of Queensland","ror":"https://ror.org/00rqy9422"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7160200397.pdf","grobid_xml":"https://content.openalex.org/works/W7160200397.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W4414815980","https://openalex.org/W4415309546","https://openalex.org/W4416775595"],"related_works":["https://openalex.org/W4403909425","https://openalex.org/W4408398943","https://openalex.org/W3092813072","https://openalex.org/W6948341469","https://openalex.org/W7162960570","https://openalex.org/W4406813958","https://openalex.org/W2098927187","https://openalex.org/W7213390280","https://openalex.org/W2766446970","https://openalex.org/W3082314342"],"abstract_inverted_index":{"The":[0],"hybrid":[1,105],"approach":[2],"to":[3,7,91,110,163,168],"experimental":[4],"design":[5,79],"aims":[6],"control":[8,45],"frequentist":[9],"operating":[10,17,47,124],"characteristics":[11,18,125],"of":[12,25,67,117,142],"Bayesian":[13,68,166],"decision":[14,97],"procedures.":[15],"These":[16],"are":[19,52,146],"assessed":[20],"by":[21,148],"simulating":[22],"sampling":[23],"distributions":[24,51],"posterior":[26,35,50,112],"summaries":[27,113],"under":[28,61,103],"assumed":[29],"data-generation":[30],"processes":[31],"that":[32,57],"also":[33],"define":[34],"distributions.":[36],"Model":[37],"misspecification":[38],"can":[39],"distort":[40],"effect":[41],"estimation":[42],"and":[43,70,76,96],"compromise":[44],"over":[46],"characteristics.":[48],"Generalized":[49],"defined":[53],"using":[54],"generalized":[55,82,101],"likelihoods":[56],"characterize":[58],"data":[59],"generation":[60],"fewer":[62],"assumptions,":[63],"enhancing":[64],"the":[65,104,118,127,140,143],"robustness":[66],"analysis":[69],"study":[71],"design.":[72],"However,":[73],"widely":[74],"applicable":[75],"computationally":[77],"efficient":[78,89],"methodology":[80,145],"with":[81,100,154],"posteriors":[83,102],"is":[84],"lacking.":[85],"We":[86],"propose":[87],"an":[88,150],"method":[90],"determine":[92],"suitable":[93],"sample":[94,119,128,137],"sizes":[95],"criteria":[98],"associated":[99],"approach.":[106],"Using":[107],"theoretical":[108],"results":[109],"model":[111],"as":[114],"linear":[115],"functions":[116],"size,":[120],"we":[121,157],"efficiently":[122],"assess":[123],"throughout":[126],"size":[129],"space":[130],"given":[131],"simulations":[132],"conducted":[133],"at":[134],"only":[135],"two":[136],"sizes.":[138],"While":[139],"benefits":[141],"proposed":[144],"illustrated":[147],"redesigning":[149],"adaptive":[151],"clinical":[152],"trial":[153],"time-to-event":[155],"outcomes,":[156],"overview":[158],"our":[159],"framework's":[160],"broader":[161],"applicability":[162],"experiments":[164],"involving":[165],"analogues":[167],"M-estimation.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-05-05T00:00:00"}