{"id":"https://openalex.org/W2031552191","doi":"https://doi.org/10.1109/ssp.2014.6884589","title":"Improving SMC sampler estimate by recycling all past simulated particles","display_name":"Improving SMC sampler estimate by recycling all past simulated particles","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2031552191","doi":"https://doi.org/10.1109/ssp.2014.6884589","mag":"2031552191"},"language":"en","primary_location":{"id":"doi:10.1109/ssp.2014.6884589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2014.6884589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Workshop on Statistical Signal Processing (SSP)","raw_type":"proceedings-article"},"type":"preprint","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/A5042101651","display_name":"Thi Le Thu Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210143894","display_name":"Laboratoire d'Automatique, G\u00e9nie Informatique et Signal","ror":"https://ror.org/04wez4p43","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I2279609970","https://openalex.org/I4210131494","https://openalex.org/I4210137101","https://openalex.org/I4210139971","https://openalex.org/I4210143894","https://openalex.org/I7454413"]},{"id":"https://openalex.org/I205703379","display_name":"Institut Mines-T\u00e9l\u00e9com","ror":"https://ror.org/025vp2923","country_code":"FR","type":"facility","lineage":["https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Thi Le Thu Nguyen","raw_affiliation_strings":["T\u00e9l\u00e9com Lille, Institut Mines-T\u00e9l\u00e9com, France","LAGIS, Inst. Mines-Telecom, Lille, France"],"affiliations":[{"raw_affiliation_string":"T\u00e9l\u00e9com Lille, Institut Mines-T\u00e9l\u00e9com, France","institution_ids":["https://openalex.org/I205703379"]},{"raw_affiliation_string":"LAGIS, Inst. Mines-Telecom, Lille, France","institution_ids":["https://openalex.org/I205703379","https://openalex.org/I4210143894"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075184549","display_name":"Fran\u00e7ois Septier","orcid":"https://orcid.org/0000-0001-5931-6091"},"institutions":[{"id":"https://openalex.org/I4210143894","display_name":"Laboratoire d'Automatique, G\u00e9nie Informatique et Signal","ror":"https://ror.org/04wez4p43","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I2279609970","https://openalex.org/I4210131494","https://openalex.org/I4210137101","https://openalex.org/I4210139971","https://openalex.org/I4210143894","https://openalex.org/I7454413"]},{"id":"https://openalex.org/I205703379","display_name":"Institut Mines-T\u00e9l\u00e9com","ror":"https://ror.org/025vp2923","country_code":"FR","type":"facility","lineage":["https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Francois Septier","raw_affiliation_strings":["T\u00e9l\u00e9com Lille, Institut Mines-T\u00e9l\u00e9com, France","LAGIS, Inst. Mines-Telecom, Lille, France"],"affiliations":[{"raw_affiliation_string":"T\u00e9l\u00e9com Lille, Institut Mines-T\u00e9l\u00e9com, France","institution_ids":["https://openalex.org/I205703379"]},{"raw_affiliation_string":"LAGIS, Inst. Mines-Telecom, Lille, France","institution_ids":["https://openalex.org/I205703379","https://openalex.org/I4210143894"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087616647","display_name":"Gareth W. Peters","orcid":"https://orcid.org/0000-0003-2768-8979"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gareth W. Peters","raw_affiliation_strings":["Department of Statistical Science, University of College London, UK","Dept. of Stat. Sci., Univ. of Coll. London, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University of College London, UK","institution_ids":[]},{"raw_affiliation_string":"Dept. of Stat. Sci., Univ. of Coll. London, London, UK","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014158998","display_name":"Yves Delignon","orcid":"https://orcid.org/0000-0001-8224-6394"},"institutions":[{"id":"https://openalex.org/I205703379","display_name":"Institut Mines-T\u00e9l\u00e9com","ror":"https://ror.org/025vp2923","country_code":"FR","type":"facility","lineage":["https://openalex.org/I205703379"]},{"id":"https://openalex.org/I4210143894","display_name":"Laboratoire d'Automatique, G\u00e9nie Informatique et Signal","ror":"https://ror.org/04wez4p43","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I2279609970","https://openalex.org/I4210131494","https://openalex.org/I4210137101","https://openalex.org/I4210139971","https://openalex.org/I4210143894","https://openalex.org/I7454413"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yves Delignon","raw_affiliation_strings":["T\u00e9l\u00e9com Lille, Institut Mines-T\u00e9l\u00e9com, France","LAGIS, Inst. Mines-Telecom, Lille, France"],"affiliations":[{"raw_affiliation_string":"T\u00e9l\u00e9com Lille, Institut Mines-T\u00e9l\u00e9com, France","institution_ids":["https://openalex.org/I205703379"]},{"raw_affiliation_string":"LAGIS, Inst. Mines-Telecom, Lille, France","institution_ids":["https://openalex.org/I205703379","https://openalex.org/I4210143894"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5042101651"],"corresponding_institution_ids":["https://openalex.org/I4210143894","https://openalex.org/I205703379"],"apc_list":null,"apc_paid":null,"fwci":0.48298105,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.7501366,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"117","last_page":"120"},"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":0.9919999837875366,"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":0.9919999837875366,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9882000088691711,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8972693681716919},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7105176448822021},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6921528577804565},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.6615029573440552},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5685699582099915},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5537046790122986},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5520249009132385},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5326399803161621},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5189483165740967},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.47847363352775574},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3927564024925232},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3826889991760254},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2517061233520508},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2514394521713257},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1669650375843048},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15194407105445862},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.07578814029693604}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8972693681716919},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7105176448822021},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6921528577804565},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.6615029573440552},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5685699582099915},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5537046790122986},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5520249009132385},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5326399803161621},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5189483165740967},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47847363352775574},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3927564024925232},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3826889991760254},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2517061233520508},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2514394521713257},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1669650375843048},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15194407105445862},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.07578814029693604},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssp.2014.6884589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2014.6884589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Workshop on Statistical Signal Processing (SSP)","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":18,"referenced_works":["https://openalex.org/W1979969656","https://openalex.org/W1995050491","https://openalex.org/W2031945974","https://openalex.org/W2040276592","https://openalex.org/W2048509028","https://openalex.org/W2055936398","https://openalex.org/W2109557612","https://openalex.org/W2129140116","https://openalex.org/W2140322433","https://openalex.org/W2147357149","https://openalex.org/W2156257099","https://openalex.org/W2912102539","https://openalex.org/W2949414641","https://openalex.org/W3098843478","https://openalex.org/W3123680396","https://openalex.org/W4292691288","https://openalex.org/W6662576158","https://openalex.org/W6843903269"],"related_works":["https://openalex.org/W2126907425","https://openalex.org/W2208639223","https://openalex.org/W2114656557","https://openalex.org/W2195963939","https://openalex.org/W1499764293","https://openalex.org/W2514372983","https://openalex.org/W1497303808","https://openalex.org/W2075503097","https://openalex.org/W3087071515","https://openalex.org/W4283077537"],"abstract_inverted_index":{"Sequential":[0],"Monte":[1,25],"Carlo":[2,26],"(SMC)":[3],"methods":[4],"are":[5],"not":[6],"only":[7],"a":[8,19,44],"popular":[9],"tool":[10],"in":[11,28,52,56,76],"the":[12,53,60,63,69,73],"analysis":[13],"of":[14,47,62],"state-space":[15],"models,":[16],"but":[17],"offer":[18],"powerful":[20],"alternative":[21],"to":[22,58],"Markov":[23],"chain":[24],"(MCMC)":[27],"situations":[29],"where":[30],"static":[31],"Bayesian":[32],"inference":[33],"must":[34],"be":[35],"performed":[36],"via":[37],"simulation.":[38],"In":[39],"this":[40],"paper,":[41],"we":[42],"propose":[43],"recycling":[45],"scheme":[46],"all":[48],"past":[49],"simulated":[50],"particles":[51],"SMC":[54],"sampler":[55],"order":[57],"reduce":[59],"variance":[61],"final":[64],"estimator.":[65],"We":[66],"demonstrate":[67],"how":[68],"proposed":[70],"approach":[71],"outperforms":[72],"classical":[74],"strategy":[75],"two":[77],"challenging":[78],"models.":[79]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}