{"id":"https://openalex.org/W2982582821","doi":"https://doi.org/10.1109/msp.2019.2938026","title":"Elucidating the Auxiliary Particle Filter via Multiple Importance Sampling [Lecture Notes]","display_name":"Elucidating the Auxiliary Particle Filter via Multiple Importance Sampling [Lecture Notes]","publication_year":2019,"publication_date":"2019-10-30","ids":{"openalex":"https://openalex.org/W2982582821","doi":"https://doi.org/10.1109/msp.2019.2938026","mag":"2982582821"},"language":"en","primary_location":{"id":"doi:10.1109/msp.2019.2938026","is_oa":false,"landing_page_url":"https://doi.org/10.1109/msp.2019.2938026","pdf_url":null,"source":{"id":"https://openalex.org/S120977877","display_name":"IEEE Signal Processing Magazine","issn_l":"1053-5888","issn":["1053-5888","1558-0792"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Magazine","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/20.500.11820/6fb3a690-8630-4a6e-92d7-b32b900d79ef","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085691944","display_name":"V\u0301\u0131ctor Elvira","orcid":"https://orcid.org/0000-0002-8967-4866"},"institutions":[{"id":"https://openalex.org/I4210133642","display_name":"IMT Nord Europe","ror":"https://ror.org/042rh9p26","country_code":"FR","type":"education","lineage":["https://openalex.org/I205703379","https://openalex.org/I4210133642"]},{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["FR","GB"],"is_corresponding":true,"raw_author_name":"Victor Elvira","raw_affiliation_strings":["Department of Communication Systems, IMT Lille Douai, France","University of Edinburgh, Edinburgh, UK"],"raw_orcid":"https://orcid.org/0000-0002-8967-4866","affiliations":[{"raw_affiliation_string":"Department of Communication Systems, IMT Lille Douai, France","institution_ids":["https://openalex.org/I4210133642"]},{"raw_affiliation_string":"University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016290792","display_name":"Luca Martino","orcid":"https://orcid.org/0000-0002-7611-6558"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Luca Martino","raw_affiliation_strings":["Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain","Statistical Signal Processing, Universidad Carlos III de Madrid, Spain"],"raw_orcid":"https://orcid.org/0000-0002-7611-6558","affiliations":[{"raw_affiliation_string":"Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain","institution_ids":["https://openalex.org/I50357001"]},{"raw_affiliation_string":"Statistical Signal Processing, Universidad Carlos III de Madrid, Spain","institution_ids":["https://openalex.org/I50357001"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058778434","display_name":"M\u00f3nica F. Bugallo","orcid":"https://orcid.org/0000-0003-2963-1474"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Monica F. Bugallo","raw_affiliation_strings":["College of Engineering and Applied Sciences, Stony Brook University, New York","Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY, USA"],"raw_orcid":"https://orcid.org/0000-0003-2963-1474","affiliations":[{"raw_affiliation_string":"College of Engineering and Applied Sciences, Stony Brook University, New York","institution_ids":["https://openalex.org/I59553526"]},{"raw_affiliation_string":"Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006962534","display_name":"Petar M. Djuri\u0107","orcid":"https://orcid.org/0000-0001-7791-3199"},"institutions":[{"id":"https://openalex.org/I17626003","display_name":"University of Rhode Island","ror":"https://ror.org/013ckk937","country_code":"US","type":"education","lineage":["https://openalex.org/I17626003"]},{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Petar M. Djuric","raw_affiliation_strings":["Electrical Engineering, University of Rhode Island, Kingston","Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering, University of Rhode Island, Kingston","institution_ids":["https://openalex.org/I17626003"]},{"raw_affiliation_string":"Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5085691944"],"corresponding_institution_ids":["https://openalex.org/I4210133642","https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":2.6132,"has_fulltext":true,"cited_by_count":46,"citation_normalized_percentile":{"value":0.92267611,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"36","issue":"6","first_page":"145","last_page":"152"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9998999834060669,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9998999834060669,"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/T11220","display_name":"Water Systems and Optimization","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9879000186920166,"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/particle-filter","display_name":"Particle filter","score":0.9030104875564575},{"id":"https://openalex.org/keywords/auxiliary-particle-filter","display_name":"Auxiliary particle filter","score":0.752048134803772},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6170667409896851},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6041439771652222},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5932295918464661},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5744203925132751},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.555202066898346},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5526118278503418},{"id":"https://openalex.org/keywords/particle","display_name":"Particle (ecology)","score":0.5289105176925659},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5020484924316406},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.49245867133140564},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30366021394729614},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28795433044433594},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24318915605545044},{"id":"https://openalex.org/keywords/ensemble-kalman-filter","display_name":"Ensemble Kalman filter","score":0.13173726201057434},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.10603466629981995},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07107767462730408}],"concepts":[{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.9030104875564575},{"id":"https://openalex.org/C52483021","wikidata":"https://www.wikidata.org/wiki/Q4827310","display_name":"Auxiliary particle filter","level":5,"score":0.752048134803772},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6170667409896851},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6041439771652222},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5932295918464661},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5744203925132751},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.555202066898346},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5526118278503418},{"id":"https://openalex.org/C2778517922","wikidata":"https://www.wikidata.org/wiki/Q7140482","display_name":"Particle (ecology)","level":2,"score":0.5289105176925659},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5020484924316406},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.49245867133140564},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30366021394729614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28795433044433594},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24318915605545044},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.13173726201057434},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.10603466629981995},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07107767462730408},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","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/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/msp.2019.2938026","is_oa":false,"landing_page_url":"https://doi.org/10.1109/msp.2019.2938026","pdf_url":null,"source":{"id":"https://openalex.org/S120977877","display_name":"IEEE Signal Processing Magazine","issn_l":"1053-5888","issn":["1053-5888","1558-0792"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Magazine","raw_type":"journal-article"},{"id":"pmh:oai:pure.ed.ac.uk:publications/6fb3a690-8630-4a6e-92d7-b32b900d79ef","is_oa":true,"landing_page_url":null,"pdf_url":"http://hdl.handle.net/20.500.11820/6fb3a690-8630-4a6e-92d7-b32b900d79ef","source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":"","raw_type":""},{"id":"pmh:oai:pure.ed.ac.uk:publications/6fb3a690-8630-4a6e-92d7-b32b900d79ef","is_oa":true,"landing_page_url":"https://hdl.handle.net/20.500.11820/6fb3a690-8630-4a6e-92d7-b32b900d79ef","pdf_url":"http://hdl.handle.net/20.500.11820/6fb3a690-8630-4a6e-92d7-b32b900d79ef","source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"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":"Elvira Arregui, V, Martino, L, Bugallo, M & Djuri\u0107, P M 2019, 'Elucidating the auxiliary particle filter via multiple importance sampling', IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 145-152. https://doi.org/10.1109/MSP.2019.2938026","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/6fb3a690-8630-4a6e-92d7-b32b900d79ef","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/6fb3a690-8630-4a6e-92d7-b32b900d79ef","pdf_url":null,"source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"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":"Elvira Arregui, V, Martino, L, Bugallo, M & Djuri\u0107, P M 2019, 'Elucidating the auxiliary particle filter via multiple importance sampling', IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 145-152. https://doi.org/10.1109/MSP.2019.2938026","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:publications/6fb3a690-8630-4a6e-92d7-b32b900d79ef","is_oa":true,"landing_page_url":null,"pdf_url":"http://hdl.handle.net/20.500.11820/6fb3a690-8630-4a6e-92d7-b32b900d79ef","source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":"","raw_type":""},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G313110997","display_name":null,"funder_award_id":"ANR-17-CE40-0031-01","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G3853206302","display_name":"CIF: Small: Advancing Adaptive Importance Sampling for Signal Processing","funder_award_id":"1617986","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4812324844","display_name":null,"funder_award_id":"CCF-1617986","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6803667113","display_name":null,"funder_award_id":"ANR-17","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G8687132392","display_name":"Adaptive importance sampling methods for Bayesian inference in complex systems","funder_award_id":"ANR-17-CE40-0031","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2982582821.pdf","grobid_xml":"https://content.openalex.org/works/W2982582821.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W88520345","https://openalex.org/W95577512","https://openalex.org/W2025863411","https://openalex.org/W2050386482","https://openalex.org/W2055936398","https://openalex.org/W2098613108","https://openalex.org/W2105934661","https://openalex.org/W2111787305","https://openalex.org/W2131598171","https://openalex.org/W2132847825","https://openalex.org/W2160337655","https://openalex.org/W2162524281","https://openalex.org/W2166323258","https://openalex.org/W2179435707","https://openalex.org/W2204230881","https://openalex.org/W2294875987","https://openalex.org/W2341285855","https://openalex.org/W2749949798","https://openalex.org/W2770385712","https://openalex.org/W2903008802","https://openalex.org/W3104127448","https://openalex.org/W4232464081","https://openalex.org/W4244486013","https://openalex.org/W4255133955","https://openalex.org/W6676780147"],"related_works":["https://openalex.org/W2368144031","https://openalex.org/W3144709167","https://openalex.org/W2355962871","https://openalex.org/W2127981223","https://openalex.org/W2162253570","https://openalex.org/W2593675237","https://openalex.org/W2109576686","https://openalex.org/W2124156864","https://openalex.org/W2381817522","https://openalex.org/W2020937726"],"abstract_inverted_index":{"Sequential":[0],"Monte":[1],"Carlo":[2],"methods,":[3],"also":[4,133,198],"known":[5,136],"as":[6,137],"particle":[7,46,69,101],"filtering,":[8,70],"have":[9],"seen":[10],"an":[11],"explosion":[12],"of":[13,22,30,42,59,77],"development":[14,60],"both":[15],"in":[16,27,35,44,105,145],"theory":[17],"and":[18,63,92,114,142],"applications.":[19],"The":[20,100,196],"publication":[21],"[1]":[23,106],"sparked":[24],"huge":[25],"interest":[26,78],"the":[28,40,98,110,122,138,148,150,166,177,187,190,203],"area":[29],"sequential":[31,36],"signal":[32],"processing,":[33],"particularly":[34],"filtering.":[37],"Ever":[38],"since,":[39],"number":[41],"publications":[43],"which":[45],"filtering":[47,170],"plays":[48],"a":[49,180],"prominent":[50],"role":[51],"has":[52],"continued":[53],"to":[54,97,153,162],"grow.":[55],"An":[56],"early":[57],"reference":[58],"is":[61,107,117,121,135,152],"[2]":[62],"later":[64],"tutorials":[65],"include":[66],"[3]-[9].":[67],"With":[68,147],"we":[71,175],"estimate":[72],"probability":[73,80],"density":[74],"functions":[75],"(pdfs)":[76],"by":[79,127],"mass":[81],"functions,":[82],"whose":[83,93],"masses":[84],"are":[85,95],"placed":[86],"at":[87,157],"randomly":[88],"chosen":[89],"locations":[90],"(particles)":[91],"weights":[94],"assigned":[96],"particles.":[99],"filter":[102,126,130],"(PF)":[103],"proposed":[104,144],"often":[108,124],"called":[109],"bootstrap":[111],"PF":[112,140],"(BPF),":[113],"although":[115],"it":[116,120],"not":[118],"optimal,":[119],"most":[123],"used":[125],"practitioners.":[128],"A":[129],"that":[131],"became":[132],"popular":[134],"auxiliary":[139],"(APF)":[141],"was":[143],"[10].":[146],"APF,":[149],"objective":[151],"generate":[154],"better":[155],"particles":[156],"each":[158],"time":[159],"step":[160],"compared":[161],"those":[163],"generated":[164],"with":[165,202],"BPF,":[167],"thereby":[168],"improving":[169],"accuracy.":[171],"In":[172],"this":[173],"article,":[174],"derive":[176],"APF":[178,188],"from":[179,189],"new":[181],"perspective,":[182],"one":[183],"based":[184],"on":[185],"interpreting":[186],"multiple":[191],"importance":[192],"sampling":[193],"(MIS)":[194],"paradigm.":[195],"derivation":[197],"shows":[199],"its":[200],"relationship":[201],"BPF.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":4}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
