{"id":"https://openalex.org/W3161345512","doi":"https://doi.org/10.1109/icassp39728.2021.9414875","title":"Particle Gibbs Sampling for Regime-Switching State-Space Models","display_name":"Particle Gibbs Sampling for Regime-Switching State-Space Models","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3161345512","doi":"https://doi.org/10.1109/icassp39728.2021.9414875","mag":"3161345512"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","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/A5069588600","display_name":"Yousef El-Laham","orcid":"https://orcid.org/0000-0002-0728-737X"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yousef El-Laham","raw_affiliation_strings":["Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY","Stony Brook University, Stony Brook, NY"],"affiliations":[{"raw_affiliation_string":"Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]},{"raw_affiliation_string":"Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100654083","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-3205-3663"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liu Yang","raw_affiliation_strings":["Stony Brook University, Stony Brook, NY"],"affiliations":[{"raw_affiliation_string":"Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078210453","display_name":"Heather J. Lynch","orcid":"https://orcid.org/0000-0002-9026-1612"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heather J. Lynch","raw_affiliation_strings":["Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY","Stony Brook University, Stony Brook, NY"],"affiliations":[{"raw_affiliation_string":"Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]},{"raw_affiliation_string":"Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006962534","display_name":"Petar M. Djuri\u0107","orcid":"https://orcid.org/0000-0001-7791-3199"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Petar M. Djuric","raw_affiliation_strings":["Stony Brook University, Stony Brook, NY"],"affiliations":[{"raw_affiliation_string":"Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058778434","display_name":"M\u00f3nica F. Bugallo","orcid":"https://orcid.org/0000-0003-2963-1474"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Monica F. Bugallo","raw_affiliation_strings":["Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY","Stony Brook University, Stony Brook, NY"],"affiliations":[{"raw_affiliation_string":"Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]},{"raw_affiliation_string":"Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5069588600"],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":0.136,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52135824,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"15","issue":null,"first_page":"5579","last_page":"5583"},"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":0.9993000030517578,"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":0.9993000030517578,"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":0.9886000156402588,"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/gibbs-sampling","display_name":"Gibbs sampling","score":0.8605443835258484},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6914110779762268},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.6800674200057983},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.6318621039390564},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6094973683357239},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5884143710136414},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.5252712965011597},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5107510089874268},{"id":"https://openalex.org/keywords/abundance","display_name":"Abundance (ecology)","score":0.424634724855423},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.41997385025024414},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.41743406653404236},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.41256067156791687},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3972499668598175},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39429640769958496},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3235301673412323},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.27378958463668823},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25961989164352417},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.16737410426139832},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13797831535339355},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.12160643935203552}],"concepts":[{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.8605443835258484},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6914110779762268},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.6800674200057983},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.6318621039390564},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6094973683357239},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5884143710136414},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.5252712965011597},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5107510089874268},{"id":"https://openalex.org/C77077793","wikidata":"https://www.wikidata.org/wiki/Q336019","display_name":"Abundance (ecology)","level":2,"score":0.424634724855423},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.41997385025024414},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.41743406653404236},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.41256067156791687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3972499668598175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39429640769958496},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3235301673412323},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27378958463668823},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25961989164352417},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.16737410426139832},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13797831535339355},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.12160643935203552},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1501586228","https://openalex.org/W1570515701","https://openalex.org/W1998625821","https://openalex.org/W1999674105","https://openalex.org/W2000032310","https://openalex.org/W2007998685","https://openalex.org/W2046278043","https://openalex.org/W2064645663","https://openalex.org/W2076818168","https://openalex.org/W2098613108","https://openalex.org/W2105934661","https://openalex.org/W2117397690","https://openalex.org/W2121448470","https://openalex.org/W2140051159","https://openalex.org/W2148061324","https://openalex.org/W2161244654","https://openalex.org/W2166323258","https://openalex.org/W2216834010","https://openalex.org/W2294875987","https://openalex.org/W2951416778","https://openalex.org/W3084375114","https://openalex.org/W3102824770","https://openalex.org/W3118048480","https://openalex.org/W4255133955","https://openalex.org/W6680456232"],"related_works":["https://openalex.org/W2103582908","https://openalex.org/W1982370455","https://openalex.org/W1517763961","https://openalex.org/W2133543800","https://openalex.org/W3122927555","https://openalex.org/W2494929626","https://openalex.org/W27102384","https://openalex.org/W3125897635","https://openalex.org/W1990075812","https://openalex.org/W3161345512"],"abstract_inverted_index":{"Regime-switching":[0],"state-space":[1,21],"models":[2,10],"(RS-SSMs)":[3],"are":[4,42],"an":[5,88],"important":[6],"class":[7],"of":[8,51,102],"statistical":[9],"that":[11],"can":[12],"be":[13],"used":[14],"to":[15,87],"represent":[16],"real-world":[17],"phenomena.":[18],"Unlike":[19],"regular":[20],"models,":[22],"RS-SSMs":[23],"allow":[24],"for":[25,48,70],"dynamic":[26],"uncertainty":[27],"in":[28,57,73,95,104],"the":[29,77,92,97,105],"state":[30],"transition":[31],"and":[32,54,99],"observations":[33],"distributions,":[34],"making":[35],"them":[36],"much":[37],"more":[38],"expressive.":[39],"Unfortunately,":[40],"there":[41],"no":[43],"existing":[44],"Bayesian":[45,71],"inference":[46,79],"techniques":[47],"joint":[49],"estimation":[50],"regimes,":[52],"states,":[53],"model":[55],"parameters":[56],"generic":[58],"RS-SSMs.":[59,74],"In":[60],"this":[61],"work,":[62],"we":[63],"develop":[64],"a":[65,82],"particle":[66],"Gibbs":[67],"sampling":[68],"algorithm":[69],"learning":[72],"We":[75],"demonstrate":[76],"proposed":[78],"approach":[80],"on":[81],"synthetic":[83],"data":[84],"experiment":[85],"related":[86],"ecological":[89],"application,":[90],"where":[91],"goal":[93],"is":[94],"estimating":[96],"abundance":[98],"demographic":[100],"rates":[101],"penguins":[103],"Antarctic.":[106]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}