{"id":"https://openalex.org/W2103335683","doi":"https://doi.org/10.1109/icassp.2006.1661375","title":"Iterative Constrained Maximum Likelihood Estimation Via Expectation Propagation","display_name":"Iterative Constrained Maximum Likelihood Estimation Via Expectation Propagation","publication_year":2006,"publication_date":"2006-08-03","ids":{"openalex":"https://openalex.org/W2103335683","doi":"https://doi.org/10.1109/icassp.2006.1661375","mag":"2103335683"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2006.1661375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2006.1661375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings","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/A5060945955","display_name":"John MacLaren Walsh","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J.M. Walsh","raw_affiliation_strings":["School of Elec. and Comp. Eng., Cornell University, Ithaca, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Elec. and Comp. Eng., Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110032726","display_name":"P.A. Regalia","orcid":null},"institutions":[{"id":"https://openalex.org/I84470341","display_name":"Catholic University of America","ror":"https://ror.org/047yk3s18","country_code":"US","type":"education","lineage":["https://openalex.org/I84470341"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"P.A. Regalia","raw_affiliation_strings":["Department of Elec. and Comp. Sci., Catholic University of America, Washington D.C., DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Elec. and Comp. Sci., Catholic University of America, Washington D.C., DC, USA","institution_ids":["https://openalex.org/I84470341"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3988,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.85077092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":null,"first_page":"V","last_page":"713"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994999766349792,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994999766349792,"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":0.9990000128746033,"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":0.9968000054359436,"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/factor-graph","display_name":"Factor graph","score":0.7934218645095825},{"id":"https://openalex.org/keywords/belief-propagation","display_name":"Belief propagation","score":0.7541249990463257},{"id":"https://openalex.org/keywords/expectation-propagation","display_name":"Expectation propagation","score":0.6862902045249939},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5514748692512512},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5413695573806763},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5008296966552734},{"id":"https://openalex.org/keywords/propagation-of-uncertainty","display_name":"Propagation of uncertainty","score":0.44774845242500305},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.43163004517555237},{"id":"https://openalex.org/keywords/reciprocity","display_name":"Reciprocity (cultural anthropology)","score":0.4246922731399536},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.40263915061950684},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37558165192604065},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.34897398948669434},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16033494472503662},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.15332061052322388}],"concepts":[{"id":"https://openalex.org/C159246509","wikidata":"https://www.wikidata.org/wiki/Q5428725","display_name":"Factor graph","level":3,"score":0.7934218645095825},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.7541249990463257},{"id":"https://openalex.org/C2779363554","wikidata":"https://www.wikidata.org/wiki/Q5420835","display_name":"Expectation propagation","level":4,"score":0.6862902045249939},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5514748692512512},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5413695573806763},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5008296966552734},{"id":"https://openalex.org/C123614077","wikidata":"https://www.wikidata.org/wiki/Q1364905","display_name":"Propagation of uncertainty","level":2,"score":0.44774845242500305},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.43163004517555237},{"id":"https://openalex.org/C169903001","wikidata":"https://www.wikidata.org/wiki/Q3264987","display_name":"Reciprocity (cultural anthropology)","level":2,"score":0.4246922731399536},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.40263915061950684},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37558165192604065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34897398948669434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16033494472503662},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.15332061052322388},{"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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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/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/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icassp.2006.1661375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2006.1661375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.318.2388","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.318.2388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ece.drexel.edu/walsh/publications/walsh/expctProp.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.334.9208","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.334.9208","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www-public.int-evry.fr/~regalia/personal/articles/icassp2006.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1501567504","https://openalex.org/W1515272691","https://openalex.org/W1541292555","https://openalex.org/W1603765807","https://openalex.org/W1934021597","https://openalex.org/W2090963365","https://openalex.org/W2109185368","https://openalex.org/W2129341461","https://openalex.org/W2137813581","https://openalex.org/W2152321006","https://openalex.org/W2159080219","https://openalex.org/W2169415915","https://openalex.org/W3127711384","https://openalex.org/W4299512035","https://openalex.org/W6640231202"],"related_works":["https://openalex.org/W2903198223","https://openalex.org/W1981023414","https://openalex.org/W2141033994","https://openalex.org/W2104914820","https://openalex.org/W2100326972","https://openalex.org/W2103335683","https://openalex.org/W4298155185","https://openalex.org/W2342270859","https://openalex.org/W2095716554","https://openalex.org/W2133237099"],"abstract_inverted_index":{"Expectation":[0,78],"propagation":[1,15,27,43,79],"defines":[2],"a":[3,52,62,85,92],"family":[4],"of":[5,41,75,95],"algorithms":[6],"for":[7,25],"approximate":[8],"Bayesian":[9],"statistical":[10],"inference":[11],"which":[12,55],"generalize":[13],"belief":[14,26],"on":[16],"factor":[17,30],"graphs":[18],"with":[19],"loops.":[20],"As":[21],"is":[22,33],"the":[23,38,72],"case":[24],"in":[28,57],"loopy":[29],"graphs,":[31],"it":[32],"not":[34],"well":[35],"understood":[36],"why":[37],"stationary":[39,73],"points":[40,70,74],"expectation":[42,76],"can":[44],"yield":[45,71],"good":[46],"estimates.":[47],"In":[48],"this":[49,96],"paper,":[50],"given":[51],"reciprocity":[53],"condition":[54],"holds":[56],"most":[58],"cases,":[59],"we":[60],"provide":[61],"constrained":[63],"maximum":[64],"likelihood":[65],"estimation":[66],"problem":[67,98],"whose":[68],"critical":[69,93],"propagation.":[77],"may":[80],"then":[81],"be":[82],"interpreted":[83],"as":[84],"nonlinear":[86],"block":[87],"Gauss":[88],"Seidel":[89],"method":[90],"seeking":[91],"point":[94],"optimization":[97]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}