{"id":"https://openalex.org/W2086374241","doi":"https://doi.org/10.1109/tsp.2012.2223691","title":"Particle Based Smoothed Marginal MAP Estimation for General State Space Models","display_name":"Particle Based Smoothed Marginal MAP Estimation for General State Space Models","publication_year":2012,"publication_date":"2012-10-09","ids":{"openalex":"https://openalex.org/W2086374241","doi":"https://doi.org/10.1109/tsp.2012.2223691","mag":"2086374241"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2012.2223691","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2012.2223691","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84714","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058607685","display_name":"Saikat Saha","orcid":"https://orcid.org/0000-0001-7920-7714"},"institutions":[{"id":"https://openalex.org/I102134673","display_name":"Link\u00f6ping University","ror":"https://ror.org/05ynxx418","country_code":"SE","type":"education","lineage":["https://openalex.org/I102134673"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Saikat Saha","raw_affiliation_strings":["Division of Automatic Control, Department of Electrical Engineering, Link\u00f6ping University, Linkoping, Sweden","Dept. of Electr. Eng. Linkoping Univ. Link\u00f6ping Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Automatic Control, Department of Electrical Engineering, Link\u00f6ping University, Linkoping, Sweden","institution_ids":["https://openalex.org/I102134673"]},{"raw_affiliation_string":"Dept. of Electr. Eng. Linkoping Univ. Link\u00f6ping Sweden","institution_ids":["https://openalex.org/I102134673"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047401338","display_name":"Pranab Kumar Mandal","orcid":"https://orcid.org/0000-0001-9131-538X"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Pranab Kumar Mandal","raw_affiliation_strings":["Department of Applied Mathematics, University of Twente, Enschede, Netherlands","Dept. of Appl. Math., Univ. of Twente, Enschede, Netherlands#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, University of Twente, Enschede, Netherlands","institution_ids":["https://openalex.org/I94624287"]},{"raw_affiliation_string":"Dept. of Appl. Math., Univ. of Twente, Enschede, Netherlands#TAB#","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113440464","display_name":"Arunabha Bagchi","orcid":null},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Arunabha Bagchi","raw_affiliation_strings":["Department of Applied Mathematics, University of Twente, Enschede, Netherlands","Dept. of Appl. Math., Univ. of Twente, Enschede, Netherlands#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, University of Twente, Enschede, Netherlands","institution_ids":["https://openalex.org/I94624287"]},{"raw_affiliation_string":"Dept. of Appl. Math., Univ. of Twente, Enschede, Netherlands#TAB#","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063795360","display_name":"Y. Boers","orcid":"https://orcid.org/0000-0002-8353-5355"},"institutions":[{"id":"https://openalex.org/I2800808753","display_name":"Thales (Netherlands)","ror":"https://ror.org/032tp7158","country_code":"NL","type":"company","lineage":["https://openalex.org/I2800808753","https://openalex.org/I4210140930"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Yvo Boers","raw_affiliation_strings":["Thales Nederland B.V., Hengelo, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Thales Nederland B.V., Hengelo, Netherlands","institution_ids":["https://openalex.org/I2800808753"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042087021","display_name":"J.N. Driessen","orcid":null},"institutions":[{"id":"https://openalex.org/I2800808753","display_name":"Thales (Netherlands)","ror":"https://ror.org/032tp7158","country_code":"NL","type":"company","lineage":["https://openalex.org/I2800808753","https://openalex.org/I4210140930"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Johannes N. Driessen","raw_affiliation_strings":["Thales Nederland B.V., Hengelo, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Thales Nederland B.V., Hengelo, Netherlands","institution_ids":["https://openalex.org/I2800808753"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3269,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.84162046,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"61","issue":"2","first_page":"264","last_page":"273"},"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":0.9998000264167786,"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":0.9998000264167786,"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":0.9993000030517578,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9965000152587891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6858506202697754},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6545605659484863},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.653910756111145},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.5410311222076416},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.532489001750946},{"id":"https://openalex.org/keywords/kernel-smoother","display_name":"Kernel smoother","score":0.5310343503952026},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5285726189613342},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.5123525857925415},{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"Marginal likelihood","score":0.492952823638916},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.48792216181755066},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.48432958126068115},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.46325501799583435},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.46041303873062134},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4302808344364166},{"id":"https://openalex.org/keywords/marginal-distribution","display_name":"Marginal distribution","score":0.42857247591018677},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3756599426269531},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3359526991844177},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.30320629477500916},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.256066232919693},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17657062411308289},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.16922104358673096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12217500805854797},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.11912143230438232}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6858506202697754},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6545605659484863},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.653910756111145},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.5410311222076416},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.532489001750946},{"id":"https://openalex.org/C27406209","wikidata":"https://www.wikidata.org/wiki/Q6394203","display_name":"Kernel smoother","level":5,"score":0.5310343503952026},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5285726189613342},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.5123525857925415},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.492952823638916},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48792216181755066},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.48432958126068115},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.46325501799583435},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.46041303873062134},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4302808344364166},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.42857247591018677},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3756599426269531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3359526991844177},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.30320629477500916},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.256066232919693},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17657062411308289},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.16922104358673096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12217500805854797},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.11912143230438232},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.0},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tsp.2012.2223691","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2012.2223691","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/e7955f20-401d-4591-8d9d-4bf945f91105","is_oa":false,"landing_page_url":"https://research.utwente.nl/en/publications/e7955f20-401d-4591-8d9d-4bf945f91105","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Saha, S, Mandal, P K, Bagchi, A, Boers, Y & Driessen, J N 2013, 'Particle based smoothed marginal MAP estimation for general state space models', IEEE transactions on signal processing, vol. 61, no. 2, pp. 264-273. https://doi.org/10.1109/TSP.2012.2223691","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:DiVA.org:liu-84714","is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84714","pdf_url":null,"source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article in journal"}],"best_oa_location":{"id":"pmh:oai:DiVA.org:liu-84714","is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84714","pdf_url":null,"source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article in journal"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5199999809265137,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1483647304","https://openalex.org/W1486578428","https://openalex.org/W1502857830","https://openalex.org/W1541249003","https://openalex.org/W1977825281","https://openalex.org/W1994691007","https://openalex.org/W2041775074","https://openalex.org/W2046399133","https://openalex.org/W2055936398","https://openalex.org/W2077611006","https://openalex.org/W2102730334","https://openalex.org/W2103908183","https://openalex.org/W2108447811","https://openalex.org/W2110744759","https://openalex.org/W2126736494","https://openalex.org/W2129078811","https://openalex.org/W2129905273","https://openalex.org/W2131255766","https://openalex.org/W2140401194","https://openalex.org/W2143737806","https://openalex.org/W2146485022","https://openalex.org/W2160337655","https://openalex.org/W2162164723","https://openalex.org/W2165609874","https://openalex.org/W2168405768","https://openalex.org/W2169040996","https://openalex.org/W2169178103","https://openalex.org/W2294875987","https://openalex.org/W2541402643","https://openalex.org/W4229793772","https://openalex.org/W4233014035","https://openalex.org/W4255133955","https://openalex.org/W6628944160","https://openalex.org/W6629209749","https://openalex.org/W6632225402","https://openalex.org/W6675569739","https://openalex.org/W6684832372"],"related_works":["https://openalex.org/W2920855166","https://openalex.org/W4205267905","https://openalex.org/W3142731338","https://openalex.org/W3149448578","https://openalex.org/W1602884087","https://openalex.org/W2764059675","https://openalex.org/W2222544391","https://openalex.org/W1976101200","https://openalex.org/W2244226369","https://openalex.org/W2104077770"],"abstract_inverted_index":{"We":[0],"consider":[1],"the":[2,26,33,42,51,54,69,73,85,96],"smoothing":[3],"problem":[4,101],"for":[5],"a":[6,46,90],"general":[7],"state":[8,52,88,103],"space":[9,104],"system":[10,92],"using":[11],"sequential":[12],"Monte":[13],"Carlo":[14],"(SMC)":[15],"methods.":[16],"The":[17,76],"marginal":[18,44,56],"smoother":[19],"is":[20,79],"assumed":[21],"to":[22,40,67,83,94],"be":[23],"available":[24],"in":[25,102],"form":[27],"of":[28,50,89,98],"weighted":[29],"random":[30],"particles":[31],"from":[32,53,72],"SMC":[34],"output.":[35],"New":[36],"algorithms":[37],"are":[38],"developed":[39],"extract":[41],"smoothed":[43],"maximum":[45],"posteriori":[47],"(MAP)":[48],"estimate":[49],"existing":[55],"particle":[57,74],"smoother.":[58,75],"Our":[59],"method":[60],"does":[61],"not":[62],"need":[63],"any":[64],"kernel":[65],"fitting":[66],"obtain":[68],"posterior":[70],"density":[71],"proposed":[77],"estimator":[78],"then":[80],"successfully":[81],"applied":[82],"find":[84],"unknown":[86],"initial":[87],"dynamical":[91],"and":[93],"address":[95],"issue":[97],"parameter":[99],"estimation":[100],"models.":[105]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}