{"id":"https://openalex.org/W2564774168","doi":"https://doi.org/10.1109/tsp.2019.2926035","title":"High-Dimensional Filtering Using Nested Sequential Monte Carlo","display_name":"High-Dimensional Filtering Using Nested Sequential Monte Carlo","publication_year":2019,"publication_date":"2019-07-07","ids":{"openalex":"https://openalex.org/W2564774168","doi":"https://doi.org/10.1109/tsp.2019.2926035","mag":"2564774168"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2019.2926035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2019.2926035","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":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1612.09162","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020493508","display_name":"Christian A. Naesseth","orcid":"https://orcid.org/0000-0002-2452-8374"},"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":true,"raw_author_name":"Christian A. Naesseth","raw_affiliation_strings":["Division of Statistics and Machine Learning, Link\u00f6ping University, Link\u00f6ping, Sweden","Division of Statistics and Machine Learning, Link\u00f6ping University, Link\u00f6ping, Sweden;"],"affiliations":[{"raw_affiliation_string":"Division of Statistics and Machine Learning, Link\u00f6ping University, Link\u00f6ping, Sweden","institution_ids":["https://openalex.org/I102134673"]},{"raw_affiliation_string":"Division of Statistics and Machine Learning, Link\u00f6ping University, Link\u00f6ping, Sweden;","institution_ids":["https://openalex.org/I102134673"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045048407","display_name":"Fredrik Lindsten","orcid":"https://orcid.org/0000-0003-3749-5820"},"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":"Fredrik Lindsten","raw_affiliation_strings":["Division of Statistics and Machine Learning, Link\u00f6ping University, Link\u00f6ping, Sweden","Division of Statistics and Machine Learning, Link\u00f6ping University, Link\u00f6ping, Sweden;"],"affiliations":[{"raw_affiliation_string":"Division of Statistics and Machine Learning, Link\u00f6ping University, Link\u00f6ping, Sweden","institution_ids":["https://openalex.org/I102134673"]},{"raw_affiliation_string":"Division of Statistics and Machine Learning, Link\u00f6ping University, Link\u00f6ping, Sweden;","institution_ids":["https://openalex.org/I102134673"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083090794","display_name":"Thomas B. Sch\u00f6n","orcid":"https://orcid.org/0000-0001-5183-234X"},"institutions":[{"id":"https://openalex.org/I123387679","display_name":"Uppsala University","ror":"https://ror.org/048a87296","country_code":"SE","type":"education","lineage":["https://openalex.org/I123387679"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Thomas B. Schon","raw_affiliation_strings":["Uppsala University, Uppsala, Sweden","Uppsala University, Uppsala (Sweden)"],"affiliations":[{"raw_affiliation_string":"Uppsala University, Uppsala, Sweden","institution_ids":["https://openalex.org/I123387679"]},{"raw_affiliation_string":"Uppsala University, Uppsala (Sweden)","institution_ids":["https://openalex.org/I123387679"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5020493508"],"corresponding_institution_ids":["https://openalex.org/I102134673"],"apc_list":null,"apc_paid":null,"fwci":0.578,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.74395954,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"67","issue":"16","first_page":"4177","last_page":"4188"},"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":0.9986000061035156,"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":0.9986000061035156,"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":0.9957000017166138,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.978600025177002,"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":0.8025833368301392},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.7444858551025391},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6739249229431152},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5726287364959717},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5493724346160889},{"id":"https://openalex.org/keywords/quasi-monte-carlo-method","display_name":"Quasi-Monte Carlo method","score":0.5297471284866333},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.5089623928070068},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5066958069801331},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49210017919540405},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4848436117172241},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.4626288115978241},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4439558982849121},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.4401341378688812},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4306320250034332},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.42634445428848267},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2777939438819885},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2738295793533325},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.09752827882766724},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07286930084228516}],"concepts":[{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.8025833368301392},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.7444858551025391},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6739249229431152},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5726287364959717},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5493724346160889},{"id":"https://openalex.org/C63320529","wikidata":"https://www.wikidata.org/wiki/Q7269435","display_name":"Quasi-Monte Carlo method","level":5,"score":0.5297471284866333},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.5089623928070068},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5066958069801331},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49210017919540405},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4848436117172241},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.4626288115978241},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4439558982849121},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.4401341378688812},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4306320250034332},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.42634445428848267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2777939438819885},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2738295793533325},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.09752827882766724},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07286930084228516}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tsp.2019.2926035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2019.2926035","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:arXiv.org:1612.09162","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1612.09162","pdf_url":"https://arxiv.org/pdf/1612.09162","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2564774168","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1612.09162","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1612.09162","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1612.09162","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1612.09162","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1612.09162","pdf_url":"https://arxiv.org/pdf/1612.09162","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1374387905","display_name":null,"funder_award_id":"2016-04278","funder_id":"https://openalex.org/F4320322581","funder_display_name":"Vetenskapsr\u00e5det"},{"id":"https://openalex.org/G166952701","display_name":null,"funder_award_id":"ICA16-0015","funder_id":"https://openalex.org/F4320320940","funder_display_name":"Stiftelsen f\u00f6r\u00a0Strategisk Forskning"},{"id":"https://openalex.org/G2295429243","display_name":null,"funder_award_id":"621-2016-06079","funder_id":"https://openalex.org/F4320322581","funder_display_name":"Vetenskapsr\u00e5det"},{"id":"https://openalex.org/G5371231072","display_name":null,"funder_award_id":"2016-06079","funder_id":"https://openalex.org/F4320322581","funder_display_name":"Vetenskapsr\u00e5det"},{"id":"https://openalex.org/G6249798265","display_name":null,"funder_award_id":"621-2016-0607","funder_id":"https://openalex.org/F4320322581","funder_display_name":"Vetenskapsr\u00e5det"},{"id":"https://openalex.org/G6535064320","display_name":null,"funder_award_id":"RIT15-0012","funder_id":"https://openalex.org/F4320322581","funder_display_name":"Vetenskapsr\u00e5det"},{"id":"https://openalex.org/G6622944701","display_name":null,"funder_award_id":"621-2016-06079","funder_id":"https://openalex.org/F4320320940","funder_display_name":"Stiftelsen f\u00f6r\u00a0Strategisk Forskning"},{"id":"https://openalex.org/G7343941351","display_name":null,"funder_award_id":"Linnaeus","funder_id":"https://openalex.org/F4320322581","funder_display_name":"Vetenskapsr\u00e5det"},{"id":"https://openalex.org/G7635396371","display_name":null,"funder_award_id":"RIT15-0012","funder_id":"https://openalex.org/F4320320940","funder_display_name":"Stiftelsen f\u00f6r\u00a0Strategisk Forskning"}],"funders":[{"id":"https://openalex.org/F4320320940","display_name":"Stiftelsen f\u00f6r\u00a0Strategisk Forskning","ror":"https://ror.org/044wr7g58"},{"id":"https://openalex.org/F4320322581","display_name":"Vetenskapsr\u00e5det","ror":"https://ror.org/03zttf063"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W143236119","https://openalex.org/W1501586228","https://openalex.org/W1529897102","https://openalex.org/W1532890487","https://openalex.org/W1607933843","https://openalex.org/W1741551153","https://openalex.org/W1822984620","https://openalex.org/W1828859704","https://openalex.org/W1860382590","https://openalex.org/W1876120984","https://openalex.org/W1975975525","https://openalex.org/W1982370455","https://openalex.org/W1983212629","https://openalex.org/W1985037657","https://openalex.org/W1988827501","https://openalex.org/W2021830477","https://openalex.org/W2032609070","https://openalex.org/W2040975855","https://openalex.org/W2043983054","https://openalex.org/W2055711980","https://openalex.org/W2064119066","https://openalex.org/W2090415144","https://openalex.org/W2098613108","https://openalex.org/W2104615575","https://openalex.org/W2105934661","https://openalex.org/W2109024167","https://openalex.org/W2111111567","https://openalex.org/W2121448470","https://openalex.org/W2150951085","https://openalex.org/W2152159624","https://openalex.org/W2152869421","https://openalex.org/W2157197146","https://openalex.org/W2163587350","https://openalex.org/W2271548160","https://openalex.org/W2402011568","https://openalex.org/W2482444618","https://openalex.org/W2513771200","https://openalex.org/W2595744296","https://openalex.org/W2962947173","https://openalex.org/W2963672032","https://openalex.org/W4232832013","https://openalex.org/W4233487859","https://openalex.org/W4235499294","https://openalex.org/W4244486013","https://openalex.org/W4250389103","https://openalex.org/W4293052541","https://openalex.org/W6640504155","https://openalex.org/W6680338314","https://openalex.org/W6697945247","https://openalex.org/W6727246412","https://openalex.org/W6790522601"],"related_works":["https://openalex.org/W2951257262","https://openalex.org/W1866173067","https://openalex.org/W1584119807","https://openalex.org/W2186202402","https://openalex.org/W2131255766","https://openalex.org/W2922027001","https://openalex.org/W2810271678","https://openalex.org/W2949679346","https://openalex.org/W2909498191","https://openalex.org/W2610095139","https://openalex.org/W3200834140","https://openalex.org/W2224884787","https://openalex.org/W2496034786","https://openalex.org/W1641947403","https://openalex.org/W1579132119","https://openalex.org/W1788219820","https://openalex.org/W2088311145","https://openalex.org/W3127819673","https://openalex.org/W2073426196","https://openalex.org/W2975985158"],"abstract_inverted_index":{"Sequential":[0],"Monte":[1,35],"Carlo":[2],"(SMC)":[3],"methods":[4,100],"comprise":[5],"one":[6],"of":[7,82],"the":[8,41,52,74,80],"most":[9],"successful":[10],"approaches":[11],"to":[12],"approximate":[13],"Bayesian":[14],"filtering.":[15],"However,":[16],"SMC":[17,42,53,62],"without":[18],"a":[19,37,60],"good":[20],"proposal":[21,54],"distribution":[22],"can":[23,67,87],"perform":[24,88],"poorly,":[25],"in":[26,28,59],"particular":[27],"high":[29],"dimensions.":[30],"We":[31,93],"propose":[32],"nested":[33],"sequential":[34],"Carlo,":[36],"methodology":[38],"that":[39],"generalizes":[40],"framework":[43],"by":[44],"requiring":[45],"only":[46],"approximate,":[47],"properly":[48],"weighted,":[49],"samples":[50],"from":[51],"distribution,":[55],"while":[56],"still":[57],"resulting":[58],"correct":[61],"algorithm.":[63],"This":[64],"way,":[65],"we":[66,86],"compute":[68],"an":[69],"\u201cexact":[70],"approximation\u201d":[71],"of,":[72],"e.g.,":[73],"locally":[75],"optimal":[76],"proposal,":[77],"and":[78],"extend":[79],"class":[81],"models":[83],"for":[84],"which":[85],"efficient":[89],"inference":[90],"using":[91],"SMC.":[92],"show":[94],"improved":[95],"accuracy":[96],"over":[97],"other":[98],"state-of-the-art":[99],"on":[101],"several":[102],"spatio-temporal":[103],"state-space":[104],"models.":[105]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
