{"id":"https://openalex.org/W3003920380","doi":"https://doi.org/10.1080/10618600.2020.1807995","title":"Fast Search and Estimation of Bayesian Nonparametric Mixture Models Using a Classification Annealing EM Algorithm","display_name":"Fast Search and Estimation of Bayesian Nonparametric Mixture Models Using a Classification Annealing EM Algorithm","publication_year":2020,"publication_date":"2020-08-14","ids":{"openalex":"https://openalex.org/W3003920380","doi":"https://doi.org/10.1080/10618600.2020.1807995","mag":"3003920380"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2020.1807995","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2020.1807995","pdf_url":null,"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"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082741454","display_name":"George Karabatsos","orcid":"https://orcid.org/0000-0003-4243-2285"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]},{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"George Karabatsos","raw_affiliation_strings":["Department of Educational Psychology, and Department of Mathematics, Statistics, and Computer Science, University of Illinois-Chicago, Chicago, IL"],"affiliations":[{"raw_affiliation_string":"Department of Educational Psychology, and Department of Mathematics, Statistics, and Computer Science, University of Illinois-Chicago, Chicago, IL","institution_ids":["https://openalex.org/I157725225","https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5082741454"],"corresponding_institution_ids":["https://openalex.org/I157725225","https://openalex.org/I39422238"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.50840979,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"30","issue":"1","first_page":"236","last_page":"247"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9998999834060669,"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":0.9998999834060669,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9591000080108643,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9506000280380249,"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/cluster-analysis","display_name":"Cluster analysis","score":0.640136182308197},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5862006545066833},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5841963291168213},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5715022087097168},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5201495289802551},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.5197622179985046},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4557662606239319},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.45470625162124634},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.4316398799419403},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35841381549835205},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2688490152359009}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.640136182308197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5862006545066833},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5841963291168213},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5715022087097168},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5201495289802551},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5197622179985046},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4557662606239319},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.45470625162124634},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.4316398799419403},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35841381549835205},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2688490152359009}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2020.1807995","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2020.1807995","pdf_url":null,"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"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W43029633","https://openalex.org/W748467075","https://openalex.org/W1560385679","https://openalex.org/W1596460436","https://openalex.org/W1907257599","https://openalex.org/W1958562800","https://openalex.org/W1987532879","https://openalex.org/W2003644411","https://openalex.org/W2006043936","https://openalex.org/W2027124963","https://openalex.org/W2029717596","https://openalex.org/W2031964861","https://openalex.org/W2036809193","https://openalex.org/W2038885294","https://openalex.org/W2041701373","https://openalex.org/W2041926198","https://openalex.org/W2045938006","https://openalex.org/W2048370764","https://openalex.org/W2049633694","https://openalex.org/W2054864979","https://openalex.org/W2059279601","https://openalex.org/W2065392216","https://openalex.org/W2069429561","https://openalex.org/W2071316194","https://openalex.org/W2072169887","https://openalex.org/W2075886515","https://openalex.org/W2080972498","https://openalex.org/W2082503527","https://openalex.org/W2095339022","https://openalex.org/W2109614047","https://openalex.org/W2119885577","https://openalex.org/W2127498532","https://openalex.org/W2136999619","https://openalex.org/W2146915717","https://openalex.org/W2151388649","https://openalex.org/W2156412372","https://openalex.org/W2266166962","https://openalex.org/W2292128399","https://openalex.org/W2408776615","https://openalex.org/W2494914260","https://openalex.org/W2509113027","https://openalex.org/W2551133224","https://openalex.org/W2553818082","https://openalex.org/W2567289819","https://openalex.org/W2572859058","https://openalex.org/W2606211438","https://openalex.org/W2790044374","https://openalex.org/W2903830437","https://openalex.org/W2916336745","https://openalex.org/W2963192864","https://openalex.org/W2963627717","https://openalex.org/W3039036645","https://openalex.org/W3101482366","https://openalex.org/W3106012925","https://openalex.org/W4236615995","https://openalex.org/W4302422232","https://openalex.org/W4308951891"],"related_works":["https://openalex.org/W2964314781","https://openalex.org/W3006565005","https://openalex.org/W4287868071","https://openalex.org/W2611832276","https://openalex.org/W4200232554","https://openalex.org/W69468016","https://openalex.org/W2007093222","https://openalex.org/W2185420989","https://openalex.org/W3124172274","https://openalex.org/W2793406240"],"abstract_inverted_index":{"Bayesian":[0],"nonparametric":[1],"(BNP)":[2],"infinite-mixture":[3],"models":[4],"provide":[5],"flexible":[6],"and":[7,13,37,69,80,144,163,175],"accurate":[8],"density":[9,75],"estimation,":[10],"cluster":[11],"analysis,":[12],"regression.":[14],"However,":[15],"for":[16,33,40,60,90,131,180,192],"the":[17,72,81,111,132,136,151,154,181,187],"posterior":[18,73,78,148],"inference":[19],"of":[20,94,103,135,140,171],"such":[21],"a":[22,45,100,128,141],"model,":[23,66],"MCMC":[24],"algorithms":[25],"are":[26,38,195],"complex,":[27],"often":[28],"need":[29],"to":[30,113],"be":[31,120],"tailor-made":[32],"different":[34],"BNP":[35,46,64,104,166],"priors,":[36],"intractable":[39],"large":[41],"datasets.":[42,177],"We":[43],"introduce":[44],"classification":[47],"annealing":[48],"EM":[49],"algorithm":[50,97,118,160,183],"which":[51],"employs":[52],"importance":[53],"sampling":[54,129],"estimation.":[55],"This":[56],"new":[57,159,182],"fast-search":[58],"algorithm,":[59],"virtually":[61],"any":[62],"given":[63,153],"mixture":[65,168],"can":[67,98,119],"quickly":[68],"accurately":[70],"calculate":[71],"predictive":[74],"estimate":[76,85],"(by":[77,86],"averaging)":[79],"maximum":[82],"a-posteriori":[83],"clustering":[84,134],"simulated":[87,173],"annealing),":[88],"even":[89],"datasets":[91],"containing":[92],"millions":[93],"observations.":[95],"The":[96,117,158],"handle":[99],"wide":[101],"range":[102],"priors":[105],"because":[106,122],"it":[107,126],"primarily":[108],"relies":[109],"on":[110],"ability":[112],"generate":[114],"prior":[115],"samples.":[116],"fast":[121],"in":[123,150,186],"each":[124],"iteration,":[125],"performs":[127,146],"step":[130],"(missing)":[133],"data":[137,174],"points,":[138],"instead":[139],"costly":[142],"E-step;":[143],"then":[145],"direct":[147],"calculations":[149],"M-step,":[152],"sampled":[155],"(imputed)":[156],"clustering.":[157],"is":[161,184],"illustrated":[162],"evaluated":[164],"through":[165],"Gaussian":[167],"model":[169],"analyses":[170],"benchmark":[172],"real":[176],"MATLAB":[178],"code":[179],"provided":[185],"supplementary":[188],"materials.":[189],"Supplementary":[190],"materials":[191],"this":[193],"article":[194],"available":[196],"online.":[197]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}