{"id":"https://openalex.org/W1999398874","doi":"https://doi.org/10.1145/2629617","title":"Bayesian Variable Selection in Linear Regression in One Pass for Large Datasets","display_name":"Bayesian Variable Selection in Linear Regression in One Pass for Large Datasets","publication_year":2014,"publication_date":"2014-08-25","ids":{"openalex":"https://openalex.org/W1999398874","doi":"https://doi.org/10.1145/2629617","mag":"1999398874","pmid":"https://pubmed.ncbi.nlm.nih.gov/37012970"},"language":"en","primary_location":{"id":"doi:10.1145/2629617","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2629617","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10066866/pdf/nihms961894.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031610238","display_name":"Carlos Ordo\u0144\u1ebdz","orcid":"https://orcid.org/0009-0005-1135-9726"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Carlos Ordonez","raw_affiliation_strings":["University of Houston"],"affiliations":[{"raw_affiliation_string":"University of Houston","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035134339","display_name":"Carlos Garcia-Alvarado","orcid":"https://orcid.org/0000-0002-9595-3861"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carlos Garcia-Alvarado","raw_affiliation_strings":["University of Houston"],"affiliations":[{"raw_affiliation_string":"University of Houston","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030846189","display_name":"Veerabhadaran Baladandayuthapani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Veerabhadaran Baladandayuthapani","raw_affiliation_strings":["UT MD Anderson Cancer Center","UT MD Anderson Cancer Center;"],"affiliations":[{"raw_affiliation_string":"UT MD Anderson Cancer Center","institution_ids":[]},{"raw_affiliation_string":"UT MD Anderson Cancer Center;","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031610238"],"corresponding_institution_ids":["https://openalex.org/I44461941"],"apc_list":null,"apc_paid":null,"fwci":1.6859,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.84316219,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"9","issue":"1","first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9990000128746033,"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":0.9990000128746033,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9961000084877014,"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/bayesian-probability","display_name":"Bayesian probability","score":0.6255238652229309},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6178643703460693},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5916250944137573},{"id":"https://openalex.org/keywords/bayesian-multivariate-linear-regression","display_name":"Bayesian multivariate linear regression","score":0.503570020198822},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.49612054228782654},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.46866023540496826},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.4497152268886566},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44613343477249146},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4395603537559509},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4314002990722656},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.38054266571998596},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3372928500175476},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.33302029967308044},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.31975623965263367}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6255238652229309},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6178643703460693},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5916250944137573},{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.503570020198822},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.49612054228782654},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.46866023540496826},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.4497152268886566},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44613343477249146},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4395603537559509},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4314002990722656},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38054266571998596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3372928500175476},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33302029967308044},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.31975623965263367},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/2629617","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2629617","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},{"id":"pmid:37012970","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37012970","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM transactions on knowledge discovery from data","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10066866","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10066866","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10066866/pdf/nihms961894.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ACM Trans Knowl Discov Data","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:10066866","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10066866","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10066866/pdf/nihms961894.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ACM Trans Knowl Discov Data","raw_type":"Text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5746292078","display_name":"III: Small:Collaborative Research: Bayesian Model Computation for Large and High Dimensional Data Sets","funder_award_id":"0915196","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6040025442","display_name":null,"funder_award_id":"IIS 0914861 and IIS 0915196","funder_id":"https://openalex.org/F4320337389","funder_display_name":"Division of Information and Intelligent Systems"},{"id":"https://openalex.org/G7806882991","display_name":null,"funder_award_id":"R01CA160736","funder_id":"https://openalex.org/F4320337351","funder_display_name":"National Cancer Institute"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8733114834","display_name":"III: Small-Collaborative: Efficient Bayesian Model Computation for Large and High Dimensional Data Sets","funder_award_id":"0914861","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337351","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81"},{"id":"https://openalex.org/F4320337389","display_name":"Division of Information and Intelligent Systems","ror":"https://ror.org/053a2cp42"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1999398874.pdf","grobid_xml":"https://content.openalex.org/works/W1999398874.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W171292237","https://openalex.org/W1596300602","https://openalex.org/W1602551265","https://openalex.org/W1760551737","https://openalex.org/W1966901553","https://openalex.org/W1989715054","https://openalex.org/W2007069447","https://openalex.org/W2019005118","https://openalex.org/W2020389170","https://openalex.org/W2022004743","https://openalex.org/W2036183522","https://openalex.org/W2045656233","https://openalex.org/W2065896400","https://openalex.org/W2094414211","https://openalex.org/W2108306139","https://openalex.org/W2129212037","https://openalex.org/W4232383088","https://openalex.org/W6606997615"],"related_works":["https://openalex.org/W2604963692","https://openalex.org/W228771216","https://openalex.org/W3100117756","https://openalex.org/W2363843476","https://openalex.org/W2606692828","https://openalex.org/W4281921183","https://openalex.org/W4309298396","https://openalex.org/W4248534646","https://openalex.org/W2074089485","https://openalex.org/W2378624038"],"abstract_inverted_index":{"Bayesian":[0,56,70],"models":[1,57],"are":[2,58,131,156],"generally":[3],"computed":[4],"with":[5,133,195,200,213],"Markov":[6],"Chain":[7],"Monte":[8],"Carlo":[9],"(MCMC)":[10],"methods.":[11],"The":[12],"main":[13,127],"disadvantage":[14],"about":[15],"MCMC":[16,89],"methods":[17],"is":[18,219],"the":[19,28,40,122,214,238],"large":[20,36],"number":[21],"of":[22,31,118,234],"iterations":[23],"they":[24],"need":[25],"to":[26,50,68,138,171,192,221],"sample":[27],"posterior":[29],"distributions":[30,101],"model":[32,71,104],"parameters,":[33,105],"especially":[34],"for":[35,73,162,185],"data":[37,108,187,202,228],"sets.":[38],"On":[39],"other":[41],"hand,":[42],"variable":[43,74,148],"selection":[44,75],"remains":[45],"a":[46,59,81,86,134,159,176],"challenging":[47],"problem":[48],"due":[49],"its":[51],"combinatorial":[52],"search":[53],"space,":[54],"where":[55],"promising":[60],"solution.":[61],"In":[62],"this":[63],"work,":[64],"we":[65],"study":[66,169],"how":[67,170],"accelerate":[69],"computation":[72],"in":[76,111,126,165],"linear":[77],"regression.":[78],"We":[79,95,168],"propose":[80],"fast":[82,163],"Gibbs":[83],"sampler":[84],"algorithm,":[85,212],"widely":[87],"used":[88],"method,":[90],"that":[91],"incorporates":[92],"several":[93,103],"optimizations.":[94],"use":[96],"non-informative":[97],"and":[98,152,189,206,231],"conjugate":[99],"prior":[100],"on":[102,144,158,227],"which":[106],"enable":[107],"set":[109,117,229],"summarization":[110,188],"one":[112],"pass":[113],"exploiting":[114,181],"an":[115],"augmented":[116],"sufficient":[119],"statistics.":[120],"Thereafter":[121],"algorithm":[123,174,218],"can":[124],"iterate":[125],"memory.":[128],"Sufficient":[129],"statistics":[130],"indexed":[132],"sparse":[135],"binary":[136],"vector":[137],"efficiently":[139],"compute":[140],"matrix":[141],"projections":[142],"based":[143],"selected":[145],"variables.":[146],"Discovered":[147],"subsets":[149],"probabilities,":[150],"selecting":[151],"discarding":[153],"each":[154],"variable,":[155],"stored":[157,190],"hash":[160],"table":[161],"retrieval":[164],"future":[166],"iterations.":[167],"integrate":[172],"our":[173,210],"into":[175],"database":[177],"management":[178],"system":[179],"(DBMS),":[180],"aggregate":[182],"User-Defined":[183],"Functions":[184],"parallel":[186],"procedures":[191],"manipulate":[193],"matrices":[194],"arrays.":[196],"An":[197],"experimental":[198],"evaluation":[199],"real":[201],"sets":[203],"evaluates":[204],"accuracy":[205],"time":[207],"performance,":[208],"comparing":[209],"DBMS-based":[211],"R":[215,239],"package.":[216,240],"Our":[217],"shown":[220],"produce":[222],"accurate":[223],"results,":[224],"scale":[225],"linearly":[226],"size":[230],"run":[232],"orders":[233],"magnitude":[235],"faster":[236],"than":[237]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
