{"id":"https://openalex.org/W7204233146","doi":"https://doi.org/10.48550/arxiv.2608.23825","title":"Efficient Bayesian Inference for Benter Models on Ranked Data","display_name":"Efficient Bayesian Inference for Benter Models on Ranked Data","publication_year":2026,"publication_date":"2026-08-24","ids":{"openalex":"https://openalex.org/W7204233146","doi":"https://doi.org/10.48550/arxiv.2608.23825"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2608.23825","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.23825","pdf_url":"https://arxiv.org/pdf/2608.23825","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-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.23825","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123825167","display_name":"Michael Pearce","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I55486353","display_name":"Reed College","ror":"https://ror.org/00a6ram87","country_code":"US","type":"education","lineage":["https://openalex.org/I55486353"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Pearce, Michael","raw_affiliation_strings":["Department of Mathematics and Statistics Reed College"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics Reed College","institution_ids":["https://openalex.org/I55486353"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5123825167"],"corresponding_institution_ids":["https://openalex.org/I55486353"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.8769999742507935,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.8769999742507935,"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":0.02019999921321869,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.013500000350177288,"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/plackett-luce-model","display_name":"Plackett-Luce model","score":0.9520000219345093},{"id":"https://openalex.org/keywords/ranking-data","display_name":"rank data","score":0.8029999732971191},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.8820000290870667},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6740000247955322},{"id":"https://openalex.org/keywords/auxiliary-variables","display_name":"auxiliary variables","score":0.6069999933242798},{"id":"https://openalex.org/keywords/stable-distributions","display_name":"stable distributions","score":0.7289999723434448},{"id":"https://openalex.org/keywords/conjugacy","display_name":"conjugacy","score":0.4699999988079071},{"id":"https://openalex.org/keywords/preference-data","display_name":"preference data","score":0.40799999237060547},{"id":"https://openalex.org/keywords/partial-rankings","display_name":"partial rankings","score":0.5120000243186951}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7182999849319458},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.652400016784668},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5805000066757202},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5788999795913696},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5609999895095825},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5139999985694885},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.45159998536109924},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.43790000677108765},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4162999987602234},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.3986000120639801},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38659998774528503},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.37790000438690186},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.36010000109672546},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3334999978542328},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.322299987077713},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.31310001015663147},{"id":"https://openalex.org/C181243257","wikidata":"https://www.wikidata.org/wiki/Q1693522","display_name":"Sample mean and sample covariance","level":3,"score":0.30000001192092896},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2924000024795532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2766000032424927},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.26980000734329224},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.23825","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.23825","pdf_url":"https://arxiv.org/pdf/2608.23825","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-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.23825","doi":"https://doi.org/10.48550/arxiv.2608.23825","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.23825","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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.23825","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.23825","pdf_url":"https://arxiv.org/pdf/2608.23825","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-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204233146.pdf","grobid_xml":"https://content.openalex.org/works/W7204233146.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"Benter":[1,75,97],"model":[2,42],"for":[3,32,73],"partial":[4,130],"and":[5,86,99,113,120,129,135],"complete":[6,128],"rankings":[7,131],"generalizes":[8],"the":[9,48,56,59,74,92,96,125],"well-known":[10],"Plackett-Luce":[11,63],"by":[12,47],"attaching":[13],"rank-level":[14],"dampening":[15,52],"parameters":[16,53],"that":[17,90],"permit":[18],"some":[19],"ranking":[20],"stages":[21],"to":[22,38,55],"be":[23],"noisier":[24],"than":[25],"others.":[26],"This":[27,65],"extension":[28],"is":[29,44],"empirically":[30],"important":[31],"domains":[33],"ranging":[34],"from":[35,132],"horse":[36],"racing":[37],"ranked-choice":[39,136],"elections.":[40],"However,":[41],"fitting":[43],"made":[45],"challenging":[46],"fractional":[49],"powers":[50],"these":[51],"introduce":[54,78],"likelihood,":[57],"breaking":[58],"conjugacy":[60],"underlying":[61],"existing":[62],"samplers.":[64],"paper":[66],"develops":[67],"an":[68],"efficient":[69,108],"Bayesian":[70],"estimation":[71],"procedure":[72],"model.":[76],"We":[77,123],"a":[79],"two-part":[80],"augmentation":[81],"scheme":[82],"using":[83],"positive":[84],"$\u03b1$-stable":[85],"exponential":[87],"auxiliary":[88],"variables":[89],"linearizes":[91],"intractable":[93],"normalizers":[94],"in":[95],"likelihood":[98],"yields":[100],"closed-form":[101],"Gibbs":[102],"updates.":[103],"A":[104],"simulation":[105],"study":[106],"confirms":[107],"estimation,":[109],"accurate":[110],"parameter":[111],"recovery,":[112],"nominal":[114],"credible-interval":[115],"coverage":[116],"across":[117],"sample":[118],"sizes":[119],"item":[121],"counts.":[122],"illustrate":[124],"algorithm":[126],"on":[127],"survey":[133],"preference":[134],"election":[137],"datasets.":[138]},"counts_by_year":[],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2026-08-27T00:00:00"}
