{"id":"https://openalex.org/W2612449910","doi":"https://doi.org/10.1109/ciss.2017.7926084","title":"Nonparametric maximum likelihood approximate message passing","display_name":"Nonparametric maximum likelihood approximate message passing","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2612449910","doi":"https://doi.org/10.1109/ciss.2017.7926084","mag":"2612449910"},"language":"en","primary_location":{"id":"doi:10.1109/ciss.2017.7926084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss.2017.7926084","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 51st Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100726625","display_name":"Long Feng","orcid":"https://orcid.org/0000-0002-2668-4805"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Long Feng","raw_affiliation_strings":["Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101883617","display_name":"Ruijun Ma","orcid":"https://orcid.org/0000-0002-6047-4246"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruijun Ma","raw_affiliation_strings":["Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011372682","display_name":"Lee H. Dicker","orcid":null},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lee H. Dicker","raw_affiliation_strings":["Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ","institution_ids":["https://openalex.org/I102322142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I102322142"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.06430738,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9991000294685364,"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9986000061035156,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7139164209365845},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6641666293144226},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.6637710928916931},{"id":"https://openalex.org/keywords/undersampling","display_name":"Undersampling","score":0.5938827991485596},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5866491198539734},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5538687109947205},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5042146444320679},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4966476559638977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4926052689552307},{"id":"https://openalex.org/keywords/message-passing","display_name":"Message passing","score":0.4492661654949188},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.4399350881576538},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.4292837977409363},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.4130690097808838},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4083235263824463},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3716774582862854},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.34512123465538025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2176164984703064},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.16360458731651306},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.15189805626869202}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7139164209365845},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6641666293144226},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6637710928916931},{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.5938827991485596},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5866491198539734},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5538687109947205},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5042146444320679},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4966476559638977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4926052689552307},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.4492661654949188},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4399350881576538},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.4292837977409363},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.4130690097808838},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4083235263824463},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3716774582862854},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.34512123465538025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2176164984703064},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.16360458731651306},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.15189805626869202},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ciss.2017.7926084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss.2017.7926084","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 51st Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1869351066","https://openalex.org/W1982226179","https://openalex.org/W2007917650","https://openalex.org/W2009511046","https://openalex.org/W2018944012","https://openalex.org/W2023584248","https://openalex.org/W2026933032","https://openalex.org/W2031775363","https://openalex.org/W2059703530","https://openalex.org/W2082029531","https://openalex.org/W2091982107","https://openalex.org/W2132660002","https://openalex.org/W2135046866","https://openalex.org/W2164595191","https://openalex.org/W2166670884","https://openalex.org/W2292172610","https://openalex.org/W2537629200","https://openalex.org/W2963159004","https://openalex.org/W2963206527","https://openalex.org/W2964082107","https://openalex.org/W2964296776","https://openalex.org/W2965130990","https://openalex.org/W4255455317","https://openalex.org/W6645648209","https://openalex.org/W6652830530","https://openalex.org/W6690309425"],"related_works":["https://openalex.org/W2109073422","https://openalex.org/W2887783772","https://openalex.org/W2101754595","https://openalex.org/W2534887053","https://openalex.org/W2026172757","https://openalex.org/W2394059563","https://openalex.org/W2017942469","https://openalex.org/W3188116148","https://openalex.org/W4249381695","https://openalex.org/W2594506622"],"abstract_inverted_index":{"Generalized":[0],"approximate":[1,114],"message":[2],"passing":[3],"(GAMP)":[4],"is":[5,58,66,117],"an":[6,84],"effective":[7],"algorithm":[8,37,43,153],"for":[9,82,136],"recovering":[10,54],"signals":[11],"from":[12,68],"noisy":[13],"linear":[14],"measurements,":[15],"assuming":[16],"known":[17],"a":[18,69,131],"priori":[19],"signal":[20,27,48,65,86,164],"distributions.":[21],"However,":[22],"in":[23,88,120,138,154],"practice,":[24],"both":[25],"the":[26,41,47,55,61,64,103,121,149],"distribution":[28,49,87],"and":[29,50,109,168],"noise":[30,51,133,166],"level":[31],"are":[32],"often":[33],"unknown.":[34],"The":[35],"EM-GM-AMP":[36,57],"integrates":[38],"GAMP":[39],"with":[40,140],"EM":[42],"to":[44,93],"simultaneously":[45],"estimate":[46],"variance":[52,134],"while":[53],"signal.":[56],"built":[59],"on":[60],"assumption":[62],"that":[63,102],"drawn":[67],"sparse":[70,122],"Gaussian":[71,123],"mixture.":[72],"In":[73,91],"this":[74,89],"paper,":[75],"we":[76,100],"propose":[77,130],"nonparametric":[78,104],"maximum":[79],"likelihood-AMP":[80],"(NPML-AMP)":[81],"estimating":[83],"arbitrary":[85],"setting.":[90],"addition":[92],"providing":[94],"more":[95],"flexibility":[96],"(and":[97],"performance":[98,150],"improvements),":[99],"argue":[101],"approach":[105],"actually":[106],"simplifies":[107],"implementation":[108],"improves":[110],"stability":[111],"by":[112],"leveraging":[113],"convexity,":[115],"which":[116],"not":[118],"available":[119],"mixture":[124],"formulation":[125],"of":[126,151],"EM-GM-AMP.":[127],"We":[128],"also":[129],"simplified":[132],"estimator":[135],"use":[137],"conjunction":[139],"NPML-AMP":[141,152],"(or":[142],"EM-GM-AMP).":[143],"A":[144],"comprehensive":[145],"numerical":[146],"study":[147],"validates":[148],"reaching":[155],"nearly":[156],"minimum":[157],"mean":[158],"squared":[159],"error":[160],"(MMSE)":[161],"under":[162],"various":[163],"distributions,":[165],"levels,":[167],"undersampling":[169],"ratios.":[170]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
