{"id":"https://openalex.org/W3212466791","doi":"https://doi.org/10.1109/mlsp52302.2021.9596490","title":"A Deep Q-Network Based Approach for Online Bayesian Change Point Detection","display_name":"A Deep Q-Network Based Approach for Online Bayesian Change Point Detection","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W3212466791","doi":"https://doi.org/10.1109/mlsp52302.2021.9596490","mag":"3212466791"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp52302.2021.9596490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101621753","display_name":"Xiaochuan Ma","orcid":"https://orcid.org/0000-0003-0362-2529"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaochuan Ma","raw_affiliation_strings":["University of California, Davis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081715462","display_name":"Lifeng Lai","orcid":"https://orcid.org/0000-0002-9493-8248"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lifeng Lai","raw_affiliation_strings":["University of California, Davis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009164482","display_name":"Shuguang Cui","orcid":"https://orcid.org/0000-0003-2608-775X"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]},{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuguang Cui","raw_affiliation_strings":["The School of Science and Engineering, Shenzhen Research Institute of Big Data and Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The School of Science and Engineering, Shenzhen Research Institute of Big Data and Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen","institution_ids":["https://openalex.org/I4210116924","https://openalex.org/I4210099586"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9944999814033508,"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"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9944999814033508,"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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9781000018119812,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9656999707221985,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/partially-observable-markov-decision-process","display_name":"Partially observable Markov decision process","score":0.8232982754707336},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.8175982236862183},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7101173400878906},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.6442670822143555},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5592484474182129},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5160117149353027},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5073563456535339},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.46233245730400085},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.4605386257171631},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4526878595352173},{"id":"https://openalex.org/keywords/observable","display_name":"Observable","score":0.4342108368873596},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38084283471107483},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36378151178359985},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3533909320831299},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.30234846472740173},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.21110615134239197},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16635191440582275}],"concepts":[{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.8232982754707336},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.8175982236862183},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7101173400878906},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.6442670822143555},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5592484474182129},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5160117149353027},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5073563456535339},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.46233245730400085},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.4605386257171631},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4526878595352173},{"id":"https://openalex.org/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.4342108368873596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38084283471107483},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36378151178359985},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3533909320831299},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.30234846472740173},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.21110615134239197},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16635191440582275},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp52302.2021.9596490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G2482887963","display_name":null,"funder_award_id":"CCF-1717943,ECCS-1711468,CNS-1824553,ECCS-2000415","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W51508254","https://openalex.org/W966080908","https://openalex.org/W1502943688","https://openalex.org/W1981904416","https://openalex.org/W2034030257","https://openalex.org/W2065623302","https://openalex.org/W2103912032","https://openalex.org/W2115211925","https://openalex.org/W2132610094","https://openalex.org/W2142851111","https://openalex.org/W2144463778","https://openalex.org/W2145339207","https://openalex.org/W2166426063","https://openalex.org/W2590781076","https://openalex.org/W2688936186","https://openalex.org/W2761707930","https://openalex.org/W2783416526","https://openalex.org/W2796511573","https://openalex.org/W2890946036","https://openalex.org/W3089919818","https://openalex.org/W3098156977","https://openalex.org/W3101076092","https://openalex.org/W3103729929","https://openalex.org/W4255089102","https://openalex.org/W6602057636","https://openalex.org/W6677067356","https://openalex.org/W6681680465"],"related_works":["https://openalex.org/W2135915258","https://openalex.org/W1760611253","https://openalex.org/W2997350486","https://openalex.org/W2146763310","https://openalex.org/W2951545791","https://openalex.org/W4287905130","https://openalex.org/W1966071689","https://openalex.org/W1581055761","https://openalex.org/W2181439950","https://openalex.org/W110662340"],"abstract_inverted_index":{"Online":[0],"quickest":[1],"change-point":[2,84],"detection":[3,68,85],"(QCD)":[4],"plays":[5],"important":[6],"role":[7],"in":[8,49],"many":[9,50],"applications":[10],"such":[11],"as":[12,26],"network":[13],"monitoring,":[14],"power":[15],"outage":[16],"detection,":[17],"etc.":[18],"Essentially,":[19],"the":[20,36,46,53,58,66,88,94,104,109],"QCD":[21,40,91],"process":[22,32],"can":[23,107],"be":[24],"viewed":[25],"a":[27,42,54,79,95],"partially":[28],"observable":[29],"Markov":[30],"decision":[31],"(POMDP).":[33],"Most":[34],"of":[35,57],"existing":[37],"works":[38],"on":[39],"assume":[41],"priori":[43,55,96],"information":[44,56,97],"about":[45],"model.":[47],"However,":[48],"practical":[51],"applications,":[52],"latent":[59],"stochastic":[60],"model":[61],"is":[62,70,98],"unknown":[63],"and":[64,113],"thus":[65],"optimal":[67],"rule":[69],"not":[71],"available.":[72],"To":[73],"address":[74],"this":[75],"issue,":[76],"we":[77],"propose":[78],"deep":[80],"Q-network":[81],"(DQN)":[82],"based":[83],"method":[86,106],"for":[87],"online":[89],"Bayesian":[90],"problem":[92],"when":[93],"unknown.":[99],"Numerical":[100],"results":[101],"illustrate":[102],"that":[103],"DQN-based":[105],"detect":[108],"change":[110],"point":[111],"accurately":[112],"timely.":[114]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
