{"id":"https://openalex.org/W4402553613","doi":"https://doi.org/10.1007/s00180-025-01637-y","title":"A simple and efficient method for sampling mixture models based on Dirichlet and Pitman-Yor processes","display_name":"A simple and efficient method for sampling mixture models based on Dirichlet and Pitman-Yor processes","publication_year":2025,"publication_date":"2025-05-31","ids":{"openalex":"https://openalex.org/W4402553613","doi":"https://doi.org/10.1007/s00180-025-01637-y"},"language":"en","primary_location":{"id":"doi:10.1007/s00180-025-01637-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-025-01637-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-025-01637-y.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00180-025-01637-y.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008926169","display_name":"Mame Diarra Fall","orcid":"https://orcid.org/0000-0003-2749-2589"},"institutions":[{"id":"https://openalex.org/I110017253","display_name":"Universit\u00e9 de Tours","ror":"https://ror.org/02wwzvj46","country_code":"FR","type":"education","lineage":["https://openalex.org/I110017253"]},{"id":"https://openalex.org/I12449238","display_name":"Universit\u00e9 d'Orl\u00e9ans","ror":"https://ror.org/014zrew76","country_code":"FR","type":"education","lineage":["https://openalex.org/I12449238"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4387156285","display_name":"Institut Denis Poisson","ror":"https://ror.org/05djhd259","country_code":null,"type":"facility","lineage":["https://openalex.org/I110017253","https://openalex.org/I12449238","https://openalex.org/I1294671590","https://openalex.org/I4387156285"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Mame Diarra Fall","raw_affiliation_strings":["Institut Denis Poisson, UMR CNRS, Universit\u00e9 d\u2019Orl\u00e9ans, Orl\u00e9ans, France","IDP - Institut Denis Poisson (Universit\u00e9 d'Orl\u00e9ans, Coll\u00e9gium Sciences et Techniques, B\u00e2timent de math\u00e9matiques - Route de Chartres, B.P. 6759 - 45067 Orl\u00e9ans cedex 2, FRANCE ; Universit\u00e9 de Tours, UFR Sciences et Techniques, Parc de Grandmont, 37200 Tours - FRANCE - France)","Institut Denis Poisson, UMR CNRS, Universit\u00e9 d'Orl\u00e9ans, Orl\u00e9ans, France"],"raw_orcid":"https://orcid.org/0000-0003-2749-2589","affiliations":[{"raw_affiliation_string":"Institut Denis Poisson, UMR CNRS, Universit\u00e9 d\u2019Orl\u00e9ans, Orl\u00e9ans, France","institution_ids":["https://openalex.org/I4387156285","https://openalex.org/I1294671590","https://openalex.org/I12449238"]},{"raw_affiliation_string":"IDP - Institut Denis Poisson (Universit\u00e9 d'Orl\u00e9ans, Coll\u00e9gium Sciences et Techniques, B\u00e2timent de math\u00e9matiques - Route de Chartres, B.P. 6759 - 45067 Orl\u00e9ans cedex 2, FRANCE ; Universit\u00e9 de Tours, UFR Sciences et Techniques, Parc de Grandmont, 37200 Tours - FRANCE - France)","institution_ids":["https://openalex.org/I4387156285","https://openalex.org/I110017253","https://openalex.org/I12449238"]},{"raw_affiliation_string":"Institut Denis Poisson, UMR CNRS, Universit\u00e9 d'Orl\u00e9ans, Orl\u00e9ans, France","institution_ids":["https://openalex.org/I4387156285","https://openalex.org/I1294671590","https://openalex.org/I12449238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022637848","display_name":"\u00c9ric Barat","orcid":"https://orcid.org/0000-0003-1113-5245"},"institutions":[{"id":"https://openalex.org/I2738703131","display_name":"Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives","ror":"https://ror.org/00jjx8s55","country_code":"FR","type":"government","lineage":["https://openalex.org/I2738703131"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I4210085861","display_name":"Laboratoire d'Int\u00e9gration des Syst\u00e8mes et des Technologies","ror":"https://ror.org/000dbcc61","country_code":"FR","type":"government","lineage":["https://openalex.org/I2738703131","https://openalex.org/I2738703131","https://openalex.org/I277688954","https://openalex.org/I4210085861","https://openalex.org/I4210117989"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"\u00c9ric Barat","raw_affiliation_strings":["Universit\u00e9 Paris Saclay, CEA, List, 91120, Palaiseau, France","DIN (CEA, LIST) - D\u00e9partement d'instrumentation Num\u00e9rique (CEA, LIST) (France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 Paris Saclay, CEA, List, 91120, Palaiseau, France","institution_ids":["https://openalex.org/I277688954","https://openalex.org/I2738703131","https://openalex.org/I4210085861"]},{"raw_affiliation_string":"DIN (CEA, LIST) - D\u00e9partement d'instrumentation Num\u00e9rique (CEA, LIST) (France)","institution_ids":["https://openalex.org/I2738703131","https://openalex.org/I4210085861"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008926169"],"corresponding_institution_ids":["https://openalex.org/I110017253","https://openalex.org/I12449238","https://openalex.org/I1294671590","https://openalex.org/I4387156285"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.00050863,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"40","issue":"8","first_page":"4675","last_page":"4716"},"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/T11152","display_name":"Stochastic processes and statistical mechanics","score":0.945900022983551,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"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/T13187","display_name":"Diffusion and Search Dynamics","score":0.9236000180244446,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.7737398743629456},{"id":"https://openalex.org/keywords/simple-random-sample","display_name":"Simple random sample","score":0.5573150515556335},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.46247541904449463},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4406633973121643},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4380286633968353},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4270769953727722},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3469024896621704},{"id":"https://openalex.org/keywords/sociology","display_name":"Sociology","score":0.08452025055885315},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.06249421834945679},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.057887881994247437},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.047697871923446655}],"concepts":[{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.7737398743629456},{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.5573150515556335},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.46247541904449463},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4406633973121643},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4380286633968353},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4270769953727722},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3469024896621704},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.08452025055885315},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.06249421834945679},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.057887881994247437},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.047697871923446655},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.0},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s00180-025-01637-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-025-01637-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-025-01637-y.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},{"id":"pmh:oai:HAL:hal-04696216v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04696216","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://hal.science/hal-04696216","raw_type":"info:eu-repo/semantics/preprint"}],"best_oa_location":{"id":"doi:10.1007/s00180-025-01637-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-025-01637-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-025-01637-y.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402553613.pdf","grobid_xml":"https://content.openalex.org/works/W4402553613.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W29489373","https://openalex.org/W73785814","https://openalex.org/W1528242606","https://openalex.org/W1560385679","https://openalex.org/W1907257599","https://openalex.org/W1966405376","https://openalex.org/W1971807270","https://openalex.org/W1981276685","https://openalex.org/W2000650629","https://openalex.org/W2006043936","https://openalex.org/W2016608935","https://openalex.org/W2018505199","https://openalex.org/W2046997730","https://openalex.org/W2051698309","https://openalex.org/W2054864979","https://openalex.org/W2060434905","https://openalex.org/W2062882942","https://openalex.org/W2063390378","https://openalex.org/W2065392216","https://openalex.org/W2070047497","https://openalex.org/W2072169887","https://openalex.org/W2079501320","https://openalex.org/W2080972498","https://openalex.org/W2082630584","https://openalex.org/W2091478817","https://openalex.org/W2091797506","https://openalex.org/W2101998432","https://openalex.org/W2118942461","https://openalex.org/W2144246192","https://openalex.org/W2163229341","https://openalex.org/W2165828043","https://openalex.org/W2169107869","https://openalex.org/W2736618479","https://openalex.org/W2810973755","https://openalex.org/W3003133111","https://openalex.org/W3099508828","https://openalex.org/W3101455611","https://openalex.org/W3106524207","https://openalex.org/W4256186701"],"related_works":["https://openalex.org/W2370014976","https://openalex.org/W2350399852","https://openalex.org/W4238714840","https://openalex.org/W3047864323","https://openalex.org/W1036777753","https://openalex.org/W2181928643","https://openalex.org/W4379210352","https://openalex.org/W3004700962","https://openalex.org/W2994963386","https://openalex.org/W2367020632"],"abstract_inverted_index":{"Abstract":[0],"We":[1],"introduce":[2],"a":[3,82],"simple":[4,83],"and":[5,16,37,63,72,84,99,109],"efficient":[6],"sampling":[7,54],"strategy":[8,86],"for":[9],"the":[10,20,28,50,89,104],"Dirichlet":[11],"process":[12,22,30],"mixture":[13,23,31],"model":[14,32],"(DPM)":[15],"its":[17,69,114],"two-parameter":[18],"extension,":[19],"Poisson-Dirichlet":[21],"model,":[24],"also":[25],"known":[26],"as":[27],"Pitman-Yor":[29],"(PYM).":[33],"Inference":[34],"in":[35],"DPM":[36],"PYM":[38],"is":[39,79,107],"typically":[40],"performed":[41],"using":[42],"Markov":[43],"Chain":[44],"Monte":[45],"Carlo":[46],"(MCMC)":[47],"methods,":[48],"specifically":[49],"Gibbs":[51],"sampler.":[52],"These":[53],"methods":[55],"are":[56],"usually":[57],"divided":[58],"into":[59],"two":[60],"classes:":[61],"marginal":[62],"conditional":[64],"algorithms.":[65],"Each":[66],"method":[67],"has":[68],"own":[70],"merits":[71],"limitations.":[73],"The":[74],"aim":[75],"of":[76,92],"this":[77],"paper":[78],"to":[80],"propose":[81],"effective":[85],"that":[87,103],"combines":[88],"main":[90],"advantages":[91],"each":[93],"class.":[94],"Extensive":[95],"experiments":[96],"on":[97],"simulated":[98],"real":[100],"data":[101],"highlight":[102],"proposed":[105],"sampler":[106],"relevant":[108],"performs":[110],"much":[111],"better":[112],"than":[113],"competitors.":[115]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}
