{"id":"https://openalex.org/W4387043673","doi":"https://doi.org/10.1080/10618600.2023.2262080","title":"Flexible Variational Bayes Based on a Copula of a Mixture","display_name":"Flexible Variational Bayes Based on a Copula of a Mixture","publication_year":2023,"publication_date":"2023-09-26","ids":{"openalex":"https://openalex.org/W4387043673","doi":"https://doi.org/10.1080/10618600.2023.2262080"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2023.2262080","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2023.2262080","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2262080?download=true","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2262080?download=true","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013464123","display_name":"David Gunawan","orcid":"https://orcid.org/0000-0002-0427-4311"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]},{"id":"https://openalex.org/I4210123005","display_name":"ARC Centre of Excellence for Mathematical and Statistical Frontiers","ror":"https://ror.org/02vcqg248","country_code":"AU","type":"facility","lineage":["https://openalex.org/I1337719021","https://openalex.org/I165779595","https://openalex.org/I2801453606","https://openalex.org/I4210123005","https://openalex.org/I4210132349"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"David Gunawan","raw_affiliation_strings":["Australian Center of Excellence for Mathematical and Statistical Frontiers, Parkville, Australia","School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Australian Center of Excellence for Mathematical and Statistical Frontiers, Parkville, Australia","institution_ids":["https://openalex.org/I4210123005"]},{"raw_affiliation_string":"School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051693933","display_name":"Robert Kohn","orcid":"https://orcid.org/0000-0002-3733-1474"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I4210123005","display_name":"ARC Centre of Excellence for Mathematical and Statistical Frontiers","ror":"https://ror.org/02vcqg248","country_code":"AU","type":"facility","lineage":["https://openalex.org/I1337719021","https://openalex.org/I165779595","https://openalex.org/I2801453606","https://openalex.org/I4210123005","https://openalex.org/I4210132349"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Robert Kohn","raw_affiliation_strings":["Australian Center of Excellence for Mathematical and Statistical Frontiers, Parkville, Australia","School of Economics, UNSW Business School, University of New South Wales, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Australian Center of Excellence for Mathematical and Statistical Frontiers, Parkville, Australia","institution_ids":["https://openalex.org/I4210123005"]},{"raw_affiliation_string":"School of Economics, UNSW Business School, University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005139027","display_name":"David J. Nott","orcid":"https://orcid.org/0000-0002-5416-0005"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"David Nott","raw_affiliation_strings":["Department of Statistics and Data Science, National University of Singapore, Singapore","Institute of Operations Research and Analytics, National University of Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]},{"raw_affiliation_string":"Institute of Operations Research and Analytics, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5013464123"],"corresponding_institution_ids":["https://openalex.org/I204824540","https://openalex.org/I4210123005"],"apc_list":null,"apc_paid":null,"fwci":0.7835,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77680375,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"33","issue":"2","first_page":"665","last_page":"680"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9997000098228455,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9973999857902527,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/copula","display_name":"Copula (linguistics)","score":0.6513857841491699},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.6234619617462158},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5410299897193909},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5277443528175354},{"id":"https://openalex.org/keywords/variance-reduction","display_name":"Variance reduction","score":0.487779438495636},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4691300094127655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44092482328414917},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4296581447124481},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4246104955673218},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4032033085823059},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3866691291332245},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3866689205169678},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37111860513687134},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.32665812969207764},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19027605652809143},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1698007583618164},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.10910150408744812}],"concepts":[{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.6513857841491699},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.6234619617462158},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5410299897193909},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5277443528175354},{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.487779438495636},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4691300094127655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44092482328414917},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4296581447124481},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4246104955673218},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4032033085823059},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3866691291332245},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3866689205169678},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37111860513687134},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32665812969207764},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19027605652809143},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1698007583618164},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.10910150408744812}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/10618600.2023.2262080","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2023.2262080","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2262080?download=true","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},{"id":"pmh:oai:ro.uow.edu.au:test2021-15613","is_oa":false,"landing_page_url":"https://ro.uow.edu.au/test2021/10066","pdf_url":null,"source":{"id":"https://openalex.org/S4306400510","display_name":"Research Online (University of Wollongong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I204824540","host_organization_name":"University of Wollongong","host_organization_lineage":["https://openalex.org/I204824540"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Harvesting Series","raw_type":"text"}],"best_oa_location":{"id":"doi:10.1080/10618600.2023.2262080","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2023.2262080","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2262080?download=true","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387043673.pdf","grobid_xml":"https://content.openalex.org/works/W4387043673.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W94194614","https://openalex.org/W114517082","https://openalex.org/W1909320841","https://openalex.org/W1970789124","https://openalex.org/W1994616650","https://openalex.org/W2003467109","https://openalex.org/W2034376463","https://openalex.org/W2059424427","https://openalex.org/W2082137964","https://openalex.org/W2108907664","https://openalex.org/W2129728285","https://openalex.org/W2144518280","https://openalex.org/W2150807068","https://openalex.org/W2225156818","https://openalex.org/W2290226688","https://openalex.org/W2398025765","https://openalex.org/W2552233335","https://openalex.org/W2579661004","https://openalex.org/W2604395440","https://openalex.org/W2743798057","https://openalex.org/W2795395625","https://openalex.org/W2945327360","https://openalex.org/W2949048600","https://openalex.org/W2951493172","https://openalex.org/W2962994101","https://openalex.org/W2963977107","https://openalex.org/W2963991960","https://openalex.org/W2964112095","https://openalex.org/W2992035660","https://openalex.org/W3011674709","https://openalex.org/W3021379655","https://openalex.org/W3043426275","https://openalex.org/W3103982962","https://openalex.org/W3104819538","https://openalex.org/W3120740533","https://openalex.org/W3129778969","https://openalex.org/W3150807214","https://openalex.org/W3159054876","https://openalex.org/W3173491913","https://openalex.org/W4235256446","https://openalex.org/W4294562888","https://openalex.org/W4299828299","https://openalex.org/W6610566761","https://openalex.org/W6631190155","https://openalex.org/W6640963894","https://openalex.org/W6682648773","https://openalex.org/W6684578138","https://openalex.org/W6763132772"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W4231274751","https://openalex.org/W1549363203","https://openalex.org/W2154063878","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1538046993","https://openalex.org/W4239293476","https://openalex.org/W2330406685"],"abstract_inverted_index":{"Variational":[0],"Bayes":[1],"methods":[2],"approximate":[3,69],"the":[4,58],"posterior":[5,74],"density":[6],"by":[7,17,46,62],"a":[8,32,38,41,52],"family":[9],"of":[10,40,57],"tractable":[11],"distributions":[12],"whose":[13],"parameters":[14],"are":[15,96],"estimated":[16],"optimization.Variational":[18],"approximation":[19,35],"is":[20,25,44,60],"useful":[21],"when":[22],"exact":[23],"inference":[24],"intractable":[26],"or":[27],"very":[28],"costly.Our":[29],"article":[30],"develops":[31],"flexible":[33],"variational":[34],"based":[36],"on":[37],"copula":[39],"mixture,":[42],"which":[43],"implemented":[45],"combining":[47],"boosting,":[48],"natural":[49],"gradient,":[50],"and":[51,65,72,90],"variance":[53],"reduction":[54],"method.The":[55],"efficacy":[56],"approach":[59],"illustrated":[61],"using":[63],"simulated":[64],"real":[66],"datasets":[67],"to":[68,79],"multimodal,":[70],"skewed":[71],"heavy-tailed":[73],"distributions,":[75],"including":[76,88],"an":[77],"application":[78],"Bayesian":[80],"deep":[81],"feedforward":[82],"neural":[83],"network":[84],"regression":[85],"models.Supplementary":[86],"materials,":[87],"appendices":[89],"computer":[91],"code":[92],"for":[93],"this":[94],"article,":[95],"available":[97],"online.":[98]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2}],"updated_date":"2026-08-23T07:36:19.812096","created_date":"2025-10-10T00:00:00"}
