{"id":"https://openalex.org/W4387882780","doi":"https://doi.org/10.1109/mlsp55844.2023.10285996","title":"Efficient Bayesian Inference By Conjugate-Computation Variational Message Passing","display_name":"Efficient Bayesian Inference By Conjugate-Computation Variational Message Passing","publication_year":2023,"publication_date":"2023-09-17","ids":{"openalex":"https://openalex.org/W4387882780","doi":"https://doi.org/10.1109/mlsp55844.2023.10285996"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp55844.2023.10285996","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/mlsp55844.2023.10285996","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.tue.nl/en/publications/970c645a-1d10-4056-83d4-b840cc2a6983","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007162821","display_name":"Mykola Lukashchuk","orcid":"https://orcid.org/0009-0003-3011-0825"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Mykola Lukashchuk","raw_affiliation_strings":["Eindhoven University of Technology,Department of Electrical Engineering","Department of Electrical Engineering, Eindhoven University of Technology"],"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology,Department of Electrical Engineering","institution_ids":["https://openalex.org/I83019370"]},{"raw_affiliation_string":"Department of Electrical Engineering, Eindhoven University of Technology","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077888111","display_name":"\u0130smai\u0307l \u015een\u00f6z","orcid":"https://orcid.org/0000-0001-7355-2138"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"\u0130smail \u015een\u00f6z","raw_affiliation_strings":["Eindhoven University of Technology,Department of Electrical Engineering","Department of Electrical Engineering, Eindhoven University of Technology"],"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology,Department of Electrical Engineering","institution_ids":["https://openalex.org/I83019370"]},{"raw_affiliation_string":"Department of Electrical Engineering, Eindhoven University of Technology","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022330444","display_name":"Bert de Vries","orcid":"https://orcid.org/0000-0003-0839-174X"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Bert de Vries","raw_affiliation_strings":["Eindhoven University of Technology,Department of Electrical Engineering","GN Hearing, Eindhoven, the Netherlands","Department of Electrical Engineering, Eindhoven University of Technology"],"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology,Department of Electrical Engineering","institution_ids":["https://openalex.org/I83019370"]},{"raw_affiliation_string":"GN Hearing, Eindhoven, the Netherlands","institution_ids":[]},{"raw_affiliation_string":"Department of Electrical Engineering, Eindhoven University of Technology","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5007162821"],"corresponding_institution_ids":["https://openalex.org/I83019370"],"apc_list":null,"apc_paid":null,"fwci":0.1728,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57750387,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9983000159263611,"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":0.9936000108718872,"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/message-passing","display_name":"Message passing","score":0.8926974534988403},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7324686646461487},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6985467672348022},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5562270283699036},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5447675585746765},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.529707670211792},{"id":"https://openalex.org/keywords/exponential-family","display_name":"Exponential family","score":0.5279635190963745},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5154422521591187},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4978818893432617},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.4633519947528839},{"id":"https://openalex.org/keywords/belief-propagation","display_name":"Belief propagation","score":0.4146541655063629},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4085982143878937},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3806415796279907},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3753258287906647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26460397243499756},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23446106910705566},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1494133174419403},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.0951370894908905},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.06613445281982422}],"concepts":[{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.8926974534988403},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7324686646461487},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6985467672348022},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5562270283699036},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5447675585746765},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.529707670211792},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.5279635190963745},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5154422521591187},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4978818893432617},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.4633519947528839},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.4146541655063629},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4085982143878937},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3806415796279907},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3753258287906647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26460397243499756},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23446106910705566},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1494133174419403},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0951370894908905},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.06613445281982422},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/mlsp55844.2023.10285996","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/mlsp55844.2023.10285996","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/970c645a-1d10-4056-83d4-b840cc2a6983","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/970c645a-1d10-4056-83d4-b840cc2a6983","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lukashchuk, M, \u0218en\u00f6z, I & de Vries, A 2023, Efficient Bayesian Inference By Conjugate-Computation Variational Message Passing. in D Comminiello & M Scarpiniti (eds), 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)., 10285996, Institute of Electrical and Electronics Engineers, pp. 1-6. https://doi.org/10.1109/MLSP55844.2023.10285996","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.tue.nl:publications/970c645a-1d10-4056-83d4-b840cc2a6983","is_oa":true,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85177240921&partnerID=8YFLogxK","pdf_url":"https://pure.tue.nl/ws/files/312944072/Efficient_Bayesian_Inference_By_Conjugate-Computation_Variational_Message_Passing.pdf","source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Lukashchuk, M, \u0218en\u00f6z, I & de Vries, A 2023, Efficient Bayesian Inference By Conjugate-Computation Variational Message Passing. in D Comminiello & M Scarpiniti (eds), 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)., 10285996, Institute of Electrical and Electronics Engineers, pp. 1-6. https://doi.org/10.1109/MLSP55844.2023.10285996","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/970c645a-1d10-4056-83d4-b840cc2a6983","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/970c645a-1d10-4056-83d4-b840cc2a6983","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lukashchuk, M, \u0218en\u00f6z, I & de Vries, A 2023, Efficient Bayesian Inference By Conjugate-Computation Variational Message Passing. in D Comminiello & M Scarpiniti (eds), 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)., 10285996, Institute of Electrical and Electronics Engineers, pp. 1-6. https://doi.org/10.1109/MLSP55844.2023.10285996","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8600000143051147}],"awards":[{"id":"https://openalex.org/G1253897967","display_name":null,"funder_award_id":"KICH3.LTP.20.006","funder_id":"https://openalex.org/F4320329456","funder_display_name":"Ministerie van Economische Zaken en Klimaat"},{"id":"https://openalex.org/G629491556","display_name":null,"funder_award_id":"(NWO)","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"},{"id":"https://openalex.org/G6640760388","display_name":null,"funder_award_id":"KICH3.LTP.20.006","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"},{"id":"https://openalex.org/F4320322835","display_name":"Ministry of Economic Affairs","ror":"https://ror.org/042ge0913"},{"id":"https://openalex.org/F4320329456","display_name":"Ministerie van Economische Zaken en Klimaat","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1530235965","https://openalex.org/W1594874645","https://openalex.org/W1970789124","https://openalex.org/W2052963087","https://openalex.org/W2119717200","https://openalex.org/W2120340025","https://openalex.org/W2139385756","https://openalex.org/W2169415915","https://openalex.org/W2485135680","https://openalex.org/W2604395440","https://openalex.org/W2801128564","https://openalex.org/W2909074583","https://openalex.org/W2949480088","https://openalex.org/W2962994101","https://openalex.org/W2963173382","https://openalex.org/W2963977107","https://openalex.org/W3102274200","https://openalex.org/W3153317535","https://openalex.org/W3176003262","https://openalex.org/W4283718553","https://openalex.org/W4289655486","https://openalex.org/W4294825671","https://openalex.org/W4299551239","https://openalex.org/W4366594996","https://openalex.org/W6635577001","https://openalex.org/W6678397451","https://openalex.org/W6679529799","https://openalex.org/W6680496554","https://openalex.org/W6682648773","https://openalex.org/W6696727771","https://openalex.org/W6735905430"],"related_works":["https://openalex.org/W2129270363","https://openalex.org/W2612895134","https://openalex.org/W2163364417","https://openalex.org/W1614580364","https://openalex.org/W2077621940","https://openalex.org/W2978729728","https://openalex.org/W4286892381","https://openalex.org/W2129340397","https://openalex.org/W2530325458","https://openalex.org/W1615740763"],"abstract_inverted_index":{"Variational":[0],"message":[1,99,110],"passing":[2,70],"is":[3,79,96],"an":[4,66,86,97],"efficient":[5,67,109],"Bayesian":[6],"inference":[7,101,112,129],"method":[8,78,125],"in":[9,43,58,93,103,113],"factorized":[10],"probabilistic":[11],"models":[12,115],"composed":[13,116],"of":[14,117],"conjugate":[15],"factors":[16,42],"from":[17],"the":[18,31,44,140],"exponential":[19],"family":[20],"(EF)":[21],"distributions.":[22],"In":[23,61],"many":[24],"applications,":[25],"a":[26],"more":[27],"accurate":[28],"model":[29],"for":[30,69,139],"process":[32],"under":[33],"consideration":[34],"can":[35],"be":[36,55],"obtained":[37],"by":[38],"inserting":[39],"nonlinear":[40,52],"deterministic":[41,75,120],"model.":[45],"Unfortunately,":[46],"variational":[47,71],"messages":[48,72,84],"that":[49],"pass":[50],"through":[51,73],"nodes":[53],"cannot":[54],"analytically":[56],"computed":[57],"closed":[59],"form.":[60],"this":[62],"paper,":[63],"we":[64],"derive":[65],"algorithm":[68,92],"arbitrary":[74,114],"factors.":[76,121],"Our":[77],"based":[80],"on":[81],"projecting":[82],"outgoing":[83],"onto":[85],"EF":[87],"distribution.":[88],"We":[89,122],"implemented":[90],"our":[91,124],"RxInfer,":[94],"which":[95],"open-source":[98],"passing-based":[100,111],"package":[102],"Julia.":[104],"The":[105],"resulting":[106],"implementation":[107],"yields":[108],"stochastic":[118],"and":[119,131],"compare":[123],"to":[126],"alternative":[127],"state-of-the-art":[128],"methods":[130],"find":[132],"lower":[133],"(i.e.,":[134],"better)":[135],"free":[136],"energy":[137],"residuals":[138],"proposed":[141],"method.":[142]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
