{"id":"https://openalex.org/W2009411433","doi":"https://doi.org/10.1109/mlsp.2014.6958847","title":"Exact Bayesian estimation in constrained Triplet Markov Chains","display_name":"Exact Bayesian estimation in constrained Triplet Markov Chains","publication_year":2014,"publication_date":"2014-09-01","ids":{"openalex":"https://openalex.org/W2009411433","doi":"https://doi.org/10.1109/mlsp.2014.6958847","mag":"2009411433"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2014.6958847","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2014.6958847","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5001420728","display_name":"Yohan Petetin","orcid":"https://orcid.org/0000-0001-9200-783X"},"institutions":[{"id":"https://openalex.org/I2738703131","display_name":"Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives","ror":"https://ror.org/00jjx8s55","country_code":"FR","type":"government","lineage":["https://openalex.org/I2738703131"]},{"id":"https://openalex.org/I4210085861","display_name":"Laboratoire d'Int\u00e9gration des Syst\u00e8mes et des Technologies","ror":"https://ror.org/000dbcc61","country_code":"FR","type":"government","lineage":["https://openalex.org/I2738703131","https://openalex.org/I2738703131","https://openalex.org/I277688954","https://openalex.org/I4210085861","https://openalex.org/I4210117989"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yohan Petetin","raw_affiliation_strings":["CEA, LIST, CEDEX, France","LADIS (CEA, LIST) - Laboratoire d'analyse des donn\u00e9es et d'intelligence des syst\u00e8mes (CEA, LIST) (anciennement LOAD ou LIMA. \r\nCEA LIST - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CEA, LIST, CEDEX, France","institution_ids":["https://openalex.org/I2738703131","https://openalex.org/I4210085861"]},{"raw_affiliation_string":"LADIS (CEA, LIST) - Laboratoire d'analyse des donn\u00e9es et d'intelligence des syst\u00e8mes (CEA, LIST) (anciennement LOAD ou LIMA. \r\nCEA LIST - France)","institution_ids":["https://openalex.org/I2738703131","https://openalex.org/I4210085861"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014563916","display_name":"Fran\u00e7ois Desbouvries","orcid":"https://orcid.org/0000-0002-2741-1569"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I135458274","display_name":"Citigroup","ror":"https://ror.org/032xgdx47","country_code":"US","type":"other","lineage":["https://openalex.org/I135458274"]},{"id":"https://openalex.org/I4210145102","display_name":"Institut Polytechnique de Paris","ror":"https://ror.org/042tfbd02","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210145102"]},{"id":"https://openalex.org/I4387153010","display_name":"T\u00e9l\u00e9com SudParis","ror":"https://ror.org/05xvk4r52","country_code":"FR","type":"education","lineage":["https://openalex.org/I205703379","https://openalex.org/I4210145102","https://openalex.org/I4387153010"]},{"id":"https://openalex.org/I4405261500","display_name":"Services r\u00e9partis, Architectures, MOd\u00e9lisation, Validation, Administration des R\u00e9seaux","ror":"https://ror.org/05d29yv45","country_code":"FR","type":"facility","lineage":["https://openalex.org/I205703379","https://openalex.org/I4210145102","https://openalex.org/I4387153010","https://openalex.org/I4405261500"]}],"countries":["FR","US"],"is_corresponding":false,"raw_author_name":"Francois Desbouvries","raw_affiliation_strings":["Telecom SudParis, CITI Department Evry, France","CNRS - Centre National de la Recherche Scientifique (France)","SAMOVAR - Services r\u00e9partis, Architectures, MOd\u00e9lisation, Validation, Administration des R\u00e9seaux (T\u00e9l\u00e9com Sudparis, 9 rue Charles Fourier -91011 Evry cedex - France)","TSP - CITI - Communications, Images et Traitement de l'Information (T\u00e9l\u00e9com SudParis - 9 rue Charles Fourier - 91011 \u00c9vry cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Telecom SudParis, CITI Department Evry, France","institution_ids":["https://openalex.org/I135458274","https://openalex.org/I4210145102"]},{"raw_affiliation_string":"CNRS - Centre National de la Recherche Scientifique (France)","institution_ids":["https://openalex.org/I1294671590"]},{"raw_affiliation_string":"SAMOVAR - Services r\u00e9partis, Architectures, MOd\u00e9lisation, Validation, Administration des R\u00e9seaux (T\u00e9l\u00e9com Sudparis, 9 rue Charles Fourier -91011 Evry cedex - France)","institution_ids":["https://openalex.org/I4210145102","https://openalex.org/I4387153010","https://openalex.org/I4405261500"]},{"raw_affiliation_string":"TSP - CITI - Communications, Images et Traitement de l'Information (T\u00e9l\u00e9com SudParis - 9 rue Charles Fourier - 91011 \u00c9vry cedex - France)","institution_ids":["https://openalex.org/I4210145102","https://openalex.org/I4387153010"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.08381205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"39","issue":null,"first_page":"1","last_page":"6"},"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":0.9987000226974487,"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":0.9987000226974487,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9916999936103821,"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/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.7027223110198975},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5538408756256104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5325222015380859},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.5072011947631836},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4862183630466461},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4813281297683716},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.48015040159225464},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.47424596548080444},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.4597288966178894},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4523603916168213},{"id":"https://openalex.org/keywords/markov-chain-mixing-time","display_name":"Markov chain mixing time","score":0.4506058692932129},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.43344298005104065},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.4322276711463928},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43042999505996704},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.41023173928260803},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37929216027259827},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.3695679306983948},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.349701464176178},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18957030773162842},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12364217638969421},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1070629358291626},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.098949134349823},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0831919014453888}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.7027223110198975},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5538408756256104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5325222015380859},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.5072011947631836},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4862183630466461},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4813281297683716},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.48015040159225464},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.47424596548080444},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.4597288966178894},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4523603916168213},{"id":"https://openalex.org/C97074811","wikidata":"https://www.wikidata.org/wiki/Q6771322","display_name":"Markov chain mixing time","level":5,"score":0.4506058692932129},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.43344298005104065},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.4322276711463928},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43042999505996704},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.41023173928260803},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37929216027259827},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.3695679306983948},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.349701464176178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18957030773162842},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12364217638969421},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1070629358291626},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.098949134349823},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0831919014453888},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mlsp.2014.6958847","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2014.6958847","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01262438v1","is_oa":false,"landing_page_url":"https://hal.science/hal-01262438","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2014 MLSP : IEEE International Workshop on Machine Learning for Signal Processing, Sep 2014, Reims, France. ⟨10.1109/MLSP.2014.6958847⟩","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1986628418","https://openalex.org/W2007321142","https://openalex.org/W2014378975","https://openalex.org/W2049633694","https://openalex.org/W2054929958","https://openalex.org/W2066045542","https://openalex.org/W2072443026","https://openalex.org/W2097009900","https://openalex.org/W2103632679","https://openalex.org/W2114690521","https://openalex.org/W2117397690","https://openalex.org/W2126736494","https://openalex.org/W2132036885","https://openalex.org/W2134324992","https://openalex.org/W2137345842","https://openalex.org/W2148061324","https://openalex.org/W3149964614","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W2027903142","https://openalex.org/W2735859191","https://openalex.org/W2987631717","https://openalex.org/W2989366387","https://openalex.org/W3166133680","https://openalex.org/W4298421644","https://openalex.org/W2107711562","https://openalex.org/W2794873331","https://openalex.org/W190459708","https://openalex.org/W50519898"],"abstract_inverted_index":{"The":[0],"Jump":[1],"Markov":[2,54],"state-space":[3],"system":[4],"(JMSS)":[5],"is":[6,23,59,90,104],"a":[7,20,51,86,98,112],"well":[8],"known":[9,114],"model":[10,22,57,115],"for":[11],"representing":[12],"dynamical":[13],"models":[14],"with":[15],"jumps.":[16],"However":[17],"inference":[18],"in":[19,26,67,92,111],"JMSS":[21,64],"NP-hard,":[24],"even":[25],"the":[27,62,93],"conditionally":[28],"linear":[29,91],"and":[30,40,66,84],"Gaussian":[31],"case.":[32],"Suboptimal":[33],"solutions":[34],"include":[35],"Sequential":[36],"Monte":[37,81],"Carlo":[38,82],"(SMC)":[39],"Interacting":[41],"Multiple":[42],"Models":[43],"(IMM)":[44],"methods.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49],"build":[50],"constrained":[52],"Triplet":[53],"Chain":[55],"(TMC)":[56],"which":[58,68,89],"close":[60],"to":[61,78],"given":[63],"model,":[65],"moments":[69],"of":[70,95,101],"interest":[71],"can":[72,107],"be":[73,108],"computed":[74],"exactly":[75],"(without":[76],"resorting":[77],"numerical":[79],"nor":[80],"approximations)":[83],"at":[85],"computational":[87],"cost":[88],"number":[94],"observations.":[96],"Additionally,":[97],"side":[99],"advantage":[100],"our":[102],"technique":[103],"that":[105],"it":[106],"used":[109],"easily":[110],"partially":[113],"context.":[116]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}