{"id":"https://openalex.org/W4205740052","doi":"https://doi.org/10.1109/tpami.2022.3140886","title":"Efficient Variational Bayes Learning of Graphical Models With Smooth Structural Changes","display_name":"Efficient Variational Bayes Learning of Graphical Models With Smooth Structural Changes","publication_year":2022,"publication_date":"2022-01-06","ids":{"openalex":"https://openalex.org/W4205740052","doi":"https://doi.org/10.1109/tpami.2022.3140886","pmid":"https://pubmed.ncbi.nlm.nih.gov/34990351"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2022.3140886","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3140886","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2009.07703","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100432180","display_name":"Hang Yu","orcid":"https://orcid.org/0000-0002-5639-0912"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Hang Yu","raw_affiliation_strings":["Nanyang Technological Unversity, Singapore","Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological Unversity, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056625821","display_name":"Songwei Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Songwei Wu","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082613025","display_name":"Justin Dauwels","orcid":"https://orcid.org/0000-0002-4390-1568"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Justin Dauwels","raw_affiliation_strings":["Electrical Engineering, Mathematics &amp; Computer Science, Delft University of Technology, Delft, CD, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering, Mathematics &amp; Computer Science, Delft University of Technology, Delft, CD, The Netherlands","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100432180"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":0.69,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.73761271,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"45","issue":"1","first_page":"475","last_page":"488"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9975000023841858,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9975000023841858,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9833999872207642,"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/notation","display_name":"Notation","score":0.6813146471977234},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.6403964757919312},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5046759843826294},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.4565843939781189},{"id":"https://openalex.org/keywords/iterated-function","display_name":"Iterated function","score":0.45383763313293457},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4403940439224243},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.4212246537208557},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.41912731528282166},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40258729457855225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.38543838262557983},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3669368028640747},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.3474752604961395},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33181798458099365},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2505335211753845}],"concepts":[{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.6813146471977234},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.6403964757919312},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5046759843826294},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.4565843939781189},{"id":"https://openalex.org/C140479938","wikidata":"https://www.wikidata.org/wiki/Q5254619","display_name":"Iterated function","level":2,"score":0.45383763313293457},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4403940439224243},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.4212246537208557},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.41912731528282166},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40258729457855225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38543838262557983},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3669368028640747},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.3474752604961395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33181798458099365},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2505335211753845},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tpami.2022.3140886","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3140886","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:34990351","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34990351","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null},{"id":"pmh:oai:arXiv.org:2009.07703","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.07703","pdf_url":"https://arxiv.org/pdf/2009.07703","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":null,"raw_type":"text"},{"id":"pmh:oai:tudelft.nl:uuid:d59b8041-2048-4cab-b5b7-33fedf316e3e","is_oa":false,"landing_page_url":"http://resolver.tudelft.nl/uuid:d59b8041-2048-4cab-b5b7-33fedf316e3e","pdf_url":null,"source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"journal article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2009.07703","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.07703","pdf_url":"https://arxiv.org/pdf/2009.07703","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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W631034941","https://openalex.org/W1519218134","https://openalex.org/W1546485431","https://openalex.org/W1549171236","https://openalex.org/W1568183599","https://openalex.org/W1629332708","https://openalex.org/W1726815243","https://openalex.org/W1754350508","https://openalex.org/W1965048988","https://openalex.org/W1965384704","https://openalex.org/W1969415786","https://openalex.org/W1970208077","https://openalex.org/W1972127338","https://openalex.org/W1988949556","https://openalex.org/W2010824638","https://openalex.org/W2021981219","https://openalex.org/W2026614436","https://openalex.org/W2028553898","https://openalex.org/W2045747994","https://openalex.org/W2050730756","https://openalex.org/W2065075214","https://openalex.org/W2075675562","https://openalex.org/W2105372391","https://openalex.org/W2110089108","https://openalex.org/W2112856361","https://openalex.org/W2112908648","https://openalex.org/W2128273936","https://openalex.org/W2131242330","https://openalex.org/W2132016554","https://openalex.org/W2132555912","https://openalex.org/W2136271315","https://openalex.org/W2138105551","https://openalex.org/W2157996961","https://openalex.org/W2159797563","https://openalex.org/W2240788666","https://openalex.org/W2395368716","https://openalex.org/W2562162676","https://openalex.org/W2593294478","https://openalex.org/W2803316390","https://openalex.org/W2904241843","https://openalex.org/W2916645399","https://openalex.org/W2949559879","https://openalex.org/W2962851448","https://openalex.org/W2963273504","https://openalex.org/W2964180340","https://openalex.org/W2964215688","https://openalex.org/W2966641445","https://openalex.org/W2969563593","https://openalex.org/W2989695133","https://openalex.org/W3004306929","https://openalex.org/W3022414451","https://openalex.org/W3082331408","https://openalex.org/W3098724218","https://openalex.org/W3098834468","https://openalex.org/W3104130497","https://openalex.org/W3122926601","https://openalex.org/W3123933399","https://openalex.org/W3124194226","https://openalex.org/W4294562888","https://openalex.org/W4301588852","https://openalex.org/W6634093112","https://openalex.org/W6637806248","https://openalex.org/W6676805969","https://openalex.org/W6676884259","https://openalex.org/W6679090966","https://openalex.org/W6679276508","https://openalex.org/W6679560219","https://openalex.org/W6682641804","https://openalex.org/W6684098844","https://openalex.org/W6684986341","https://openalex.org/W6712080681","https://openalex.org/W6783001623"],"related_works":["https://openalex.org/W2375684291","https://openalex.org/W2354676191","https://openalex.org/W3188646203","https://openalex.org/W2909957174","https://openalex.org/W136674370","https://openalex.org/W4246278799","https://openalex.org/W2105527480","https://openalex.org/W2052387497","https://openalex.org/W2132461435","https://openalex.org/W1992534732"],"abstract_inverted_index":{"Estimating":[0],"a":[1,96,100,186,256],"sequence":[2],"of":[3,16,30,49,164,176,191,213,237,249,258,324,330],"dynamic":[4,55],"undirected":[5],"graphical":[6,56,290,309],"models,":[7,291],"in":[8,19,149,211,251,263],"which":[9],"adjacent":[10],"graphs":[11,118],"share":[12],"similar":[13],"structures,":[14],"is":[15,138,166],"paramount":[17],"importance":[18],"various":[20],"social,":[21],"financial,":[22],"biological,":[23],"and":[24,45,67,91,113,124,157,184,244,271,306,334,349],"engineering":[25],"systems,":[26],"since":[27],"the":[28,47,59,64,68,117,143,147,155,158,161,172,192,196,222,230,233,247,252,266,272,279,284,288,327,337,347,350],"evolution":[29],"such":[31,119],"networks":[32,339],"can":[33,296,343],"be":[34,72,297],"utilized":[35],"for":[36,53,88,311],"example":[37],"to":[38,71,141,154,181,194,232,242,287,299],"spot":[39],"trends,":[40],"detect":[41],"anomalies,":[42],"predict":[43],"vulnerability,":[44],"evaluate":[46],"impact":[48],"interventions.":[50],"Existing":[51],"methods":[52,80,207],"learning":[54,300],"models":[57,310],"require":[58],"tuning":[60],"parameters":[61],"that":[62,120,203,246,294,336],"control":[63],"graph":[65,144],"sparsity":[66],"temporal":[69],"smoothness":[70],"selected":[73],"via":[74],"brute-force":[75],"grid":[76],"search.":[77],"Furthermore,":[78],"these":[79],"are":[81,122],"computationally":[82],"burdensome":[83],"with":[84,171,265],"time":[85,93,162,259,314],"complexity":[86,163],"O(NP<sup>3</sup>)":[87],"P":[89,224],"variables":[90],"N":[92],"points.":[94],"As":[95,316],"remedy,":[97],"we":[98,108,179,292,319],"propose":[99,185],"low-complexity":[101],"tuning-free":[102],"Bayesian":[103],"approach,":[104],"named":[105],"BASS.":[106],"Specifically,":[107],"impose":[109],"temporally":[110],"dependent":[111],"spike":[112],"slab":[114],"priors":[115],"on":[116,135,189,208],"they":[121],"sparse":[123],"varying":[125],"smoothly":[126],"across":[127],"time.":[128],"An":[129],"efficient":[130,219],"variational":[131,177],"inference":[132],"algorithm":[133],"based":[134,188],"natural":[136],"gradients":[137],"then":[139],"derived":[140],"learn":[142],"structures":[145],"from":[146,240],"data":[148,210,236],"an":[150,317],"automatic":[151],"manner.":[152],"Owing":[153],"pseudo-likelihood":[156],"mean-field":[159],"approximation,":[160],"BASS":[165,204,295,342],"only":[167],"O(NP<sup>2</sup>).":[168],"To":[169],"cope":[170],"local":[173],"maxima":[174],"problem":[175],"inference,":[178],"resort":[180],"simulated":[182],"annealing":[183,197],"method":[187],"bootstrapping":[190],"observations":[193],"generate":[195],"noise.":[198],"We":[199,227],"provide":[200],"numerical":[201],"evidence":[202],"outperforms":[205],"existing":[206],"synthetic":[209],"terms":[212],"structure":[214],"estimation,":[215],"while":[216],"being":[217],"more":[218],"especially":[220],"when":[221],"dimension":[223],"becomes":[225],"high.":[226],"further":[228,307],"apply":[229],"approach":[231],"stock":[234],"return":[235],"78":[238],"banks":[239],"2005":[241],"2013":[243],"find":[245],"number":[248],"edges":[250],"financial":[253,269],"network":[254],"as":[255],"function":[257],"contains":[260],"three":[261],"peaks,":[262],"coincidence":[264],"2008":[267],"global":[268],"crisis":[270],"two":[273],"subsequent":[274],"European":[275],"debt":[276],"crisis.":[277],"On":[278],"other":[280],"hand,":[281],"by":[282,341],"identifying":[283],"frequency-domain":[285],"resemblance":[286],"time-varying":[289],"show":[293,335],"extended":[298],"frequency-varying":[301],"inverse":[302],"spectral":[303],"density":[304],"matrices,":[305],"yields":[308],"multivariate":[312],"stationary":[313],"series.":[315],"illustration,":[318],"analyze":[320],"scalp":[321],"EEG":[322],"signals":[323],"patients":[325,348],"at":[326],"early":[328],"stages":[329],"Alzheimer's":[331],"disease":[332],"(AD)":[333],"brain":[338],"extracted":[340],"better":[344],"distinguish":[345],"between":[346],"healthy":[351],"controls.":[352]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}