{"id":"https://openalex.org/W4383499492","doi":"https://doi.org/10.1080/10618600.2023.2231514","title":"Conditional Particle Filters with Bridge Backward Sampling","display_name":"Conditional Particle Filters with Bridge Backward Sampling","publication_year":2023,"publication_date":"2023-07-07","ids":{"openalex":"https://openalex.org/W4383499492","doi":"https://doi.org/10.1080/10618600.2023.2231514"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2023.2231514","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2023.2231514","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2231514?needAccess=true&role=button","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2231514?needAccess=true&role=button","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023958929","display_name":"Santeri Karppinen","orcid":"https://orcid.org/0000-0002-4578-3147"},"institutions":[{"id":"https://openalex.org/I94722563","display_name":"University of Jyv\u00e4skyl\u00e4","ror":"https://ror.org/05n3dz165","country_code":"FI","type":"education","lineage":["https://openalex.org/I94722563"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Santeri Karppinen","raw_affiliation_strings":["Department of Mathematics and Statistics, University of Jyv\u00e4skyl\u00e4, Jyv\u00e4skyl\u00e4, Finland"],"raw_orcid":"https://orcid.org/0000-0002-4578-3147","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, University of Jyv\u00e4skyl\u00e4, Jyv\u00e4skyl\u00e4, Finland","institution_ids":["https://openalex.org/I94722563"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035598384","display_name":"Sumeetpal S. Singh","orcid":"https://orcid.org/0000-0002-5430-1496"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sumeetpal S. Singh","raw_affiliation_strings":["NIASRA, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia"],"raw_orcid":"https://orcid.org/0000-0002-5430-1496","affiliations":[{"raw_affiliation_string":"NIASRA, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079368387","display_name":"Matti Vihola","orcid":"https://orcid.org/0000-0002-8041-7222"},"institutions":[{"id":"https://openalex.org/I94722563","display_name":"University of Jyv\u00e4skyl\u00e4","ror":"https://ror.org/05n3dz165","country_code":"FI","type":"education","lineage":["https://openalex.org/I94722563"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Matti Vihola","raw_affiliation_strings":["Department of Mathematics and Statistics, University of Jyv\u00e4skyl\u00e4, Jyv\u00e4skyl\u00e4, Finland"],"raw_orcid":"https://orcid.org/0000-0002-8041-7222","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, University of Jyv\u00e4skyl\u00e4, Jyv\u00e4skyl\u00e4, Finland","institution_ids":["https://openalex.org/I94722563"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7846,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.76880502,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"33","issue":"2","first_page":"364","last_page":"378"},"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":0.9977999925613403,"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":0.9977999925613403,"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":0.9961000084877014,"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":0.9908000230789185,"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":0.5299635529518127},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5056092739105225},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.44381722807884216},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.43991583585739136},{"id":"https://openalex.org/keywords/particle","display_name":"Particle (ecology)","score":0.42476195096969604},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42161625623703003},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4090287685394287},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40216487646102905},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.3646112084388733},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3396055996417999},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.32070034742355347},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.22005176544189453},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.19521594047546387},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.1709081530570984},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.12023994326591492},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.0929049551486969}],"concepts":[{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.5299635529518127},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5056092739105225},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.44381722807884216},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.43991583585739136},{"id":"https://openalex.org/C2778517922","wikidata":"https://www.wikidata.org/wiki/Q7140482","display_name":"Particle (ecology)","level":2,"score":0.42476195096969604},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42161625623703003},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4090287685394287},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40216487646102905},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3646112084388733},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3396055996417999},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.32070034742355347},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.22005176544189453},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.19521594047546387},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.1709081530570984},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12023994326591492},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.0929049551486969},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"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/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/10618600.2023.2231514","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2023.2231514","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2231514?needAccess=true&role=button","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},{"id":"pmh:oai:jyx.jyu.fi:123456789/96812","is_oa":true,"landing_page_url":"http://urn.fi/URN:NBN:fi:jyu-202408285697","pdf_url":"https://jyx.jyu.fi/bitstreams/f8038790-1463-4e0f-8b88-3eba2bfdd415/download","source":{"id":"https://openalex.org/S4306400563","display_name":"Jyv\u00e4skyl\u00e4 University Digital Archive (University of Jyv\u00e4skyl\u00e4)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I94722563","host_organization_name":"University of Jyv\u00e4skyl\u00e4","host_organization_lineage":["https://openalex.org/I94722563"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"research article"},{"id":"pmh:oai:ro.uow.edu.au:test2021-14930","is_oa":false,"landing_page_url":"https://ro.uow.edu.au/test2021/9383","pdf_url":null,"source":{"id":"https://openalex.org/S4306400510","display_name":"Research Online (University of Wollongong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I204824540","host_organization_name":"University of Wollongong","host_organization_lineage":["https://openalex.org/I204824540"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Harvesting Series","raw_type":"text"}],"best_oa_location":{"id":"doi:10.1080/10618600.2023.2231514","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2023.2231514","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2231514?needAccess=true&role=button","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8001495319","display_name":null,"funder_award_id":"315619","funder_id":"https://openalex.org/F4320321108","funder_display_name":"Academy of Finland"},{"id":"https://openalex.org/G8862978756","display_name":"Finnish centre of excellence in Randomness and STructures (FiRST)","funder_award_id":"346311","funder_id":"https://openalex.org/F4320321108","funder_display_name":"Academy of Finland"}],"funders":[{"id":"https://openalex.org/F4320321108","display_name":"Academy of Finland","ror":"https://ror.org/05k73zm37"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4383499492.pdf"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W568632016","https://openalex.org/W776338369","https://openalex.org/W803478749","https://openalex.org/W1501586228","https://openalex.org/W1570515701","https://openalex.org/W1846292136","https://openalex.org/W1932940155","https://openalex.org/W1963568208","https://openalex.org/W2080842868","https://openalex.org/W2085738358","https://openalex.org/W2090415144","https://openalex.org/W2092124742","https://openalex.org/W2124333705","https://openalex.org/W2127855947","https://openalex.org/W2132072575","https://openalex.org/W2137306058","https://openalex.org/W2146793276","https://openalex.org/W2161633452","https://openalex.org/W2197799680","https://openalex.org/W2198904917","https://openalex.org/W2228341146","https://openalex.org/W2755019879","https://openalex.org/W2767272505","https://openalex.org/W2801625792","https://openalex.org/W2949724480","https://openalex.org/W2951416778","https://openalex.org/W2963039334","https://openalex.org/W2963156137","https://openalex.org/W2963292708","https://openalex.org/W3088532215","https://openalex.org/W3101168972","https://openalex.org/W3102824770","https://openalex.org/W3122140649","https://openalex.org/W3135773817","https://openalex.org/W3200586981","https://openalex.org/W4210619933","https://openalex.org/W4231057675","https://openalex.org/W4233487859","https://openalex.org/W4235295823","https://openalex.org/W4251382670","https://openalex.org/W4312072352","https://openalex.org/W6680456232","https://openalex.org/W7037105134"],"related_works":["https://openalex.org/W2347749309","https://openalex.org/W2384389541","https://openalex.org/W2145647377","https://openalex.org/W2742914308","https://openalex.org/W3157383594","https://openalex.org/W2117643935","https://openalex.org/W2124156864","https://openalex.org/W2381817522","https://openalex.org/W2143520177","https://openalex.org/W2145781311"],"abstract_inverted_index":{"Conditional":[0],"particle":[1],"filters":[2],"(CPFs)":[3],"with":[4,37,63,74,121,137,169],"backward/ancestor":[5,93],"sampling":[6,11,53,94,139],"are":[7,147,198],"powerful":[8],"methods":[9,35],"for":[10,78,108,158,195],"from":[12],"the":[13,17,31,92,109,113,118,127,135,167,174,186],"posterior":[14],"distribution":[15],"of":[16,20,33,48,134],"latent":[18],"states":[19],"a":[21,26,132,150,170],"dynamic":[22],"model":[23],"such":[24],"as":[25],"hidden":[27,64],"Markov":[28,65],"model.":[29],"However,":[30],"performance":[32],"these":[34,49],"deteriorates":[36],"models":[38,66],"involving":[39],"weakly":[40,79,110,187],"informative":[41,80,111,188],"observations":[42,81],"and/or":[43],"slowly":[44,88],"mixing":[45,89,191],"dynamics.":[46],"Both":[47],"complications":[50],"arise":[51],"when":[52],"finely":[54],"time-discretised":[55],"continuous-time":[56],"path":[57],"integral":[58],"models,":[59],"but":[60],"can":[61,179],"occur":[62],"too.":[67],"Multinomial":[68],"resampling,":[69],"which":[70],"is":[71],"commonly":[72],"employed":[73],"CPFs,":[75],"resamples":[76],"excessively":[77],"and":[82,117,173,189],"thereby":[83],"introduces":[84],"extra":[85],"variance.":[86],"Furthermore,":[87],"dynamics":[90],"render":[91],"steps":[95,145],"ineffective,":[96],"leading":[97],"to":[98,181],"degeneracy":[99,128],"issues.":[100],"We":[101,153],"detail":[102],"two":[103],"conditional":[104],"resampling":[105,116,120,172],"strategies":[106,157],"suitable":[107,171],"regime:":[112],"so-called":[114],"\u2018killing\u2019":[115],"systematic":[119],"mean":[122],"partial":[123],"order.":[124],"To":[125],"avoid":[126],"issues,":[129],"we":[130],"introduce":[131],"generalisation":[133],"CPF":[136,144,168],"backward":[138,177],"that":[140,146,166],"involves":[141],"auxiliary":[142],"\u2018bridging\u2019":[143],"parameterised":[148],"by":[149],"blocking":[151],"sequence.":[152],"present":[154],"practical":[155],"tuning":[156],"choosing":[159],"an":[160],"appropriate":[161],"blocking.":[162],"Our":[163],"experiments":[164],"demonstrate":[165],"developed":[175],"\u2018bridge":[176],"sampling\u2019":[178],"lead":[180],"substantial":[182],"efficiency":[183],"gains":[184],"in":[185],"slow":[190],"regime.":[192],"Supplementary":[193],"materials":[194],"this":[196],"article":[197],"available":[199],"online.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
