{"id":"https://openalex.org/W3213383573","doi":"https://doi.org/10.1109/mlsp52302.2021.9596329","title":"Message Passing-Based Inference in the Gamma Mixture Model","display_name":"Message Passing-Based Inference in the Gamma Mixture Model","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W3213383573","doi":"https://doi.org/10.1109/mlsp52302.2021.9596329","mag":"3213383573"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp52302.2021.9596329","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596329","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5071375305","display_name":"Albert Podusenko","orcid":null},"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":"Albert Podusenko","raw_affiliation_strings":["TU Eindhoven"],"affiliations":[{"raw_affiliation_string":"TU Eindhoven","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088106677","display_name":"Bart van Erp","orcid":"https://orcid.org/0000-0002-5619-7071"},"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":"Bart van Erp","raw_affiliation_strings":["TU Eindhoven"],"affiliations":[{"raw_affiliation_string":"TU Eindhoven","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080299484","display_name":"Dmitry Bagaev","orcid":"https://orcid.org/0000-0001-9655-7986"},"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":"Dmitry Bagaev","raw_affiliation_strings":["TU Eindhoven"],"affiliations":[{"raw_affiliation_string":"TU Eindhoven","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":"Ismail Senoz","raw_affiliation_strings":["TU Eindhoven"],"affiliations":[{"raw_affiliation_string":"TU Eindhoven","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":["GN Hearing","TU Eindhoven"],"affiliations":[{"raw_affiliation_string":"GN Hearing","institution_ids":[]},{"raw_affiliation_string":"TU Eindhoven","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5071375305"],"corresponding_institution_ids":["https://openalex.org/I83019370"],"apc_list":null,"apc_paid":null,"fwci":0.6113,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70757214,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"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/T11321","display_name":"Error Correcting Code Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11321","display_name":"Error Correcting Code Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9980000257492065,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9909999966621399,"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/inference","display_name":"Inference","score":0.7332701683044434},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6999955177307129},{"id":"https://openalex.org/keywords/factor-graph","display_name":"Factor graph","score":0.6998767852783203},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.6367301940917969},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.593494713306427},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5750003457069397},{"id":"https://openalex.org/keywords/generalized-gamma-distribution","display_name":"Generalized gamma distribution","score":0.5044785737991333},{"id":"https://openalex.org/keywords/gamma-distribution","display_name":"Gamma distribution","score":0.4884308874607086},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4486531913280487},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4482349753379822},{"id":"https://openalex.org/keywords/expectation-propagation","display_name":"Expectation propagation","score":0.440265953540802},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.43481117486953735},{"id":"https://openalex.org/keywords/message-passing","display_name":"Message passing","score":0.42877358198165894},{"id":"https://openalex.org/keywords/mixture-distribution","display_name":"Mixture distribution","score":0.4210679829120636},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.35035693645477295},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3155174255371094},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31127434968948364},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.21876278519630432},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19070959091186523},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.1762595772743225},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.07548236846923828}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7332701683044434},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6999955177307129},{"id":"https://openalex.org/C159246509","wikidata":"https://www.wikidata.org/wiki/Q5428725","display_name":"Factor graph","level":3,"score":0.6998767852783203},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.6367301940917969},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.593494713306427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5750003457069397},{"id":"https://openalex.org/C42337464","wikidata":"https://www.wikidata.org/wiki/Q5532478","display_name":"Generalized gamma distribution","level":3,"score":0.5044785737991333},{"id":"https://openalex.org/C149717495","wikidata":"https://www.wikidata.org/wiki/Q117806","display_name":"Gamma distribution","level":2,"score":0.4884308874607086},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4486531913280487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4482349753379822},{"id":"https://openalex.org/C2779363554","wikidata":"https://www.wikidata.org/wiki/Q5420835","display_name":"Expectation propagation","level":4,"score":0.440265953540802},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.43481117486953735},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.42877358198165894},{"id":"https://openalex.org/C56672385","wikidata":"https://www.wikidata.org/wiki/Q17157111","display_name":"Mixture distribution","level":3,"score":0.4210679829120636},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.35035693645477295},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3155174255371094},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31127434968948364},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.21876278519630432},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19070959091186523},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.1762595772743225},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.07548236846923828},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/mlsp52302.2021.9596329","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596329","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/86882666-3975-4347-88a2-ab3f2227e6bd","is_oa":false,"landing_page_url":"https://research.tue.nl/en/publications/86882666-3975-4347-88a2-ab3f2227e6bd","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Podusenko, A, van Erp, B, Bagaev, D, \u015een\u00f6z, \u0130 & Vries, B D 2021, Message Passing-Based Inference in the Gamma Mixture Model. in 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)., 9596329, Institute of Electrical and Electronics Engineers, 31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021, Gold Coast, Queensland, Australia, 25/10/21. https://doi.org/10.1109/MLSP52302.2021.9596329","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.tue.nl:publications/86882666-3975-4347-88a2-ab3f2227e6bd","is_oa":false,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85122794753&partnerID=8YFLogxK","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Podusenko, A, van Erp, B, Bagaev, D, \u015een\u00f6z, \u0130 & Vries, B D 2021, Message Passing-Based Inference in the Gamma Mixture Model. in 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)., 9596329, Institute of Electrical and Electronics Engineers, 31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021, Gold Coast, Queensland, Australia, 25/10/21. https://doi.org/10.1109/MLSP52302.2021.9596329","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:tue:oai:pure.tue.nl:publications/86882666-3975-4347-88a2-ab3f2227e6bd","is_oa":false,"landing_page_url":"https://research.tue.nl/nl/publications/86882666-3975-4347-88a2-ab3f2227e6bd","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"info:eu-repo/semantics/conferencepaper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1514444828","https://openalex.org/W1601795611","https://openalex.org/W1977750622","https://openalex.org/W1989954041","https://openalex.org/W2026628102","https://openalex.org/W2052963087","https://openalex.org/W2091990110","https://openalex.org/W2106131716","https://openalex.org/W2120340025","https://openalex.org/W2120575449","https://openalex.org/W2137813581","https://openalex.org/W2139584447","https://openalex.org/W2150859678","https://openalex.org/W2156094048","https://openalex.org/W2156358825","https://openalex.org/W2162457363","https://openalex.org/W2187089797","https://openalex.org/W2898045070","https://openalex.org/W2899947052","https://openalex.org/W3116438773","https://openalex.org/W4232464081","https://openalex.org/W4250589301","https://openalex.org/W6678397451","https://openalex.org/W6682671827","https://openalex.org/W6683068184","https://openalex.org/W6683826400","https://openalex.org/W6787627616"],"related_works":["https://openalex.org/W2999176848","https://openalex.org/W2612895134","https://openalex.org/W2129270363","https://openalex.org/W4300567170","https://openalex.org/W2903198223","https://openalex.org/W2581655336","https://openalex.org/W2104914820","https://openalex.org/W2141033994","https://openalex.org/W2978729728","https://openalex.org/W3213383573"],"abstract_inverted_index":{"The":[0,63,85],"Gamma":[1,21,48,79,86],"mixture":[2,22,49,80,87],"model":[3,23,88],"is":[4,28],"a":[5,47,93],"flexible":[6],"probability":[7],"distribution":[8],"for":[9,24,72,102],"representing":[10],"beliefs":[11],"about":[12],"scale":[13],"variables":[14,27],"such":[15],"as":[16,30,82],"precisions.":[17],"Inference":[18],"in":[19,46,69,92],"the":[20,60],"all":[25],"latent":[26],"non-trivial":[29],"it":[31],"leads":[32],"to":[33,58],"intractable":[34],"equations.":[35],"This":[36],"paper":[37],"presents":[38],"two":[39],"variants":[40],"of":[41],"variational":[42],"message":[43],"passing-based":[44],"inference":[45,68],"model.":[50],"We":[51],"use":[52],"moment":[53],"matching":[54],"and":[55,97,105],"alternatively":[56],"expectation-maximization":[57],"approximate":[59],"posterior":[61],"distributions.":[62],"proposed":[64],"method":[65],"supports":[66],"automated":[67],"factor":[70,94],"graphs":[71],"large":[73],"probabilistic":[74],"models":[75,81],"that":[76],"contain":[77],"multiple":[78],"plug-in":[83],"factors.":[84],"has":[89],"been":[90],"implemented":[91],"graph":[95],"package":[96],"we":[98],"present":[99],"experimental":[100],"results":[101],"both":[103],"synthetic":[104],"real-world":[106],"data":[107],"sets.":[108]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
