{"id":"https://openalex.org/W2070045706","doi":"https://doi.org/10.1109/ita.2012.6181768","title":"Exchangeable inconsistent priors for Bayesian posterior inference","display_name":"Exchangeable inconsistent priors for Bayesian posterior inference","publication_year":2012,"publication_date":"2012-02-01","ids":{"openalex":"https://openalex.org/W2070045706","doi":"https://doi.org/10.1109/ita.2012.6181768","mag":"2070045706"},"language":"en","primary_location":{"id":"doi:10.1109/ita.2012.6181768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ita.2012.6181768","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 Information Theory and Applications Workshop","raw_type":"proceedings-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/A5087368991","display_name":"Max Welling","orcid":"https://orcid.org/0000-0003-1484-2121"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Max Welling","raw_affiliation_strings":["Department of Computer Science, University of California, Irvine, Irvine, CA, USA","Dept. of Computer Science, UC Irvine, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of California, Irvine, Irvine, CA, USA","institution_ids":["https://openalex.org/I204250578"]},{"raw_affiliation_string":"Dept. of Computer Science, UC Irvine, CA, USA","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088860761","display_name":"Ian R. Porteous","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ian Porteous","raw_affiliation_strings":["Google, Inc., Kirkland, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google, Inc., Kirkland, WA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111730565","display_name":"Kenichi Kurihara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kenichi Kurihara","raw_affiliation_strings":["Google, Inc., Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google, Inc., Tokyo, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8846,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.7998688,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"18","issue":null,"first_page":"407","last_page":"414"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9998000264167786,"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":0.9998000264167786,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9955000281333923,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9865999817848206,"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/prior-probability","display_name":"Prior probability","score":0.9691368937492371},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.7301074266433716},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6900150775909424},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6272658109664917},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.6135038733482361},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6021769642829895},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.575526237487793},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5474118590354919},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5206100940704346},{"id":"https://openalex.org/keywords/hierarchical-dirichlet-process","display_name":"Hierarchical Dirichlet process","score":0.4610641896724701},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44753292202949524},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4355732500553131},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3008960485458374},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2373238503932953},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.1666678488254547},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.13455215096473694}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.9691368937492371},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.7301074266433716},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6900150775909424},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6272658109664917},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.6135038733482361},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6021769642829895},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.575526237487793},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5474118590354919},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5206100940704346},{"id":"https://openalex.org/C141318989","wikidata":"https://www.wikidata.org/wiki/Q5753066","display_name":"Hierarchical Dirichlet process","level":4,"score":0.4610641896724701},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44753292202949524},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4355732500553131},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3008960485458374},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2373238503932953},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.1666678488254547},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.13455215096473694},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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:10.1109/ita.2012.6181768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ita.2012.6181768","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 Information Theory and Applications Workshop","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.228.4807","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.228.4807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ics.uci.edu/%7Ewelling/publications/papers/FlexPriors_ITA12.pdf","raw_type":"text"}],"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":19,"referenced_works":["https://openalex.org/W1548714966","https://openalex.org/W1551893515","https://openalex.org/W1967687583","https://openalex.org/W1975493578","https://openalex.org/W2043285245","https://openalex.org/W2062882942","https://openalex.org/W2080972498","https://openalex.org/W2082630584","https://openalex.org/W2113893266","https://openalex.org/W2116064496","https://openalex.org/W2116825644","https://openalex.org/W2128002512","https://openalex.org/W2158266063","https://openalex.org/W2964184712","https://openalex.org/W3120740533","https://openalex.org/W4245916018","https://openalex.org/W6638444801","https://openalex.org/W6676881485","https://openalex.org/W7038517902"],"related_works":["https://openalex.org/W3121467232","https://openalex.org/W2393894849","https://openalex.org/W2107192713","https://openalex.org/W2027714503","https://openalex.org/W2544994990","https://openalex.org/W4254467987","https://openalex.org/W3190171609","https://openalex.org/W2118036030","https://openalex.org/W1531847006","https://openalex.org/W2170902875"],"abstract_inverted_index":{"Nonparametric":[0],"Bayesian":[1],"methods":[2],"offer":[3],"a":[4,45],"convenient":[5],"paradigm":[6],"to":[7,38,72,88,108],"deal":[8],"with":[9],"uncertain":[10],"model":[11],"structure.":[12],"However,":[13],"priors":[14,52,84,104],"such":[15],"as":[16],"the":[17,25,55,61,90],"(hierarchical)":[18],"Dirichlet":[19],"process":[20,28],"prior":[21,29,41],"on":[22,30,98],"partitions":[23],"and":[24,68,74,85,93],"Indian":[26],"buffet":[27],"binary":[31],"matrices":[32],"are":[33],"not":[34],"always":[35],"flexible":[36,83,103],"enough":[37],"express":[39],"our":[40,110],"beliefs.":[42],"We":[43,59],"propose":[44,69],"much":[46],"larger":[47],"family":[48],"of":[49,57,63,66],"nonparametric":[50],"exchangeable":[51],"by":[53],"relaxing":[54],"concept":[56],"consistency.":[58],"discuss":[60],"consequences":[62],"this":[64],"point":[65],"view":[67],"novel":[70],"ways":[71],"specify":[73],"learn":[75],"these":[76],"priors.":[77],"In":[78],"particular,":[79],"we":[80],"introduce":[81],"new":[82],"inference":[86],"procedures":[87],"extend":[89],"DP,":[91],"HDP":[92],"IBP":[94],"models.":[95],"An":[96],"experiment":[97],"text":[99],"data":[100],"illustrates":[101],"how":[102],"can":[105],"be":[106],"useful":[107],"increase":[109],"modeling":[111],"capabilities.":[112]},"counts_by_year":[{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}