{"id":"https://openalex.org/W2800714754","doi":"https://doi.org/10.1109/tcyb.2018.2829440","title":"Variational Bayesian Approach for Causality and Contemporaneous Correlation Features Inference in Industrial Process Data","display_name":"Variational Bayesian Approach for Causality and Contemporaneous Correlation Features Inference in Industrial Process Data","publication_year":2018,"publication_date":"2018-05-03","ids":{"openalex":"https://openalex.org/W2800714754","doi":"https://doi.org/10.1109/tcyb.2018.2829440","mag":"2800714754","pmid":"https://pubmed.ncbi.nlm.nih.gov/29994020"},"language":"en","primary_location":{"id":"doi:10.1109/tcyb.2018.2829440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2018.2829440","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"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 Cybernetics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5068935902","display_name":"Rahul Raveendran","orcid":"https://orcid.org/0000-0003-4747-2972"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Rahul Raveendran","raw_affiliation_strings":["Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada"],"raw_orcid":"https://orcid.org/0000-0003-4747-2972","affiliations":[{"raw_affiliation_string":"Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100432851","display_name":"Biao Huang","orcid":"https://orcid.org/0000-0001-9082-2216"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Biao Huang","raw_affiliation_strings":["Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada"],"raw_orcid":"https://orcid.org/0000-0001-9082-2216","affiliations":[{"raw_affiliation_string":"Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada","institution_ids":["https://openalex.org/I154425047"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154425047"],"apc_list":null,"apc_paid":null,"fwci":1.2783,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.80485425,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"49","issue":"7","first_page":"2580","last_page":"2590"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9998000264167786,"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":0.9998000264167786,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5290402770042419},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5107550621032715},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.46671101450920105},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.45997145771980286},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4576219320297241},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.45548999309539795},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.4535183012485504},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44508031010627747},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4444258511066437},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.438973069190979},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42086362838745117},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34515249729156494},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3233112692832947},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.27703750133514404}],"concepts":[{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5290402770042419},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5107550621032715},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.46671101450920105},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.45997145771980286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4576219320297241},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.45548999309539795},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.4535183012485504},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44508031010627747},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4444258511066437},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.438973069190979},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42086362838745117},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34515249729156494},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3233112692832947},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27703750133514404},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tcyb.2018.2829440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2018.2829440","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"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 Cybernetics","raw_type":"journal-article"},{"id":"pmid:29994020","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29994020","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 cybernetics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320325651","display_name":"Alberta Innovates","ror":null},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1578899157","https://openalex.org/W1966411627","https://openalex.org/W2010626297","https://openalex.org/W2012914747","https://openalex.org/W2028435021","https://openalex.org/W2055266204","https://openalex.org/W2082665707","https://openalex.org/W2084408974","https://openalex.org/W2116031726","https://openalex.org/W2120211304","https://openalex.org/W2125027820","https://openalex.org/W2133704721","https://openalex.org/W2136562407","https://openalex.org/W2139701068","https://openalex.org/W2146247053","https://openalex.org/W2151454335","https://openalex.org/W2159632462","https://openalex.org/W2160964245","https://openalex.org/W2178225550","https://openalex.org/W2343292936","https://openalex.org/W2549646667","https://openalex.org/W4240253383","https://openalex.org/W4244577065","https://openalex.org/W4388322883","https://openalex.org/W6682350343","https://openalex.org/W6696835939"],"related_works":["https://openalex.org/W2171218219","https://openalex.org/W1972271943","https://openalex.org/W2150410159","https://openalex.org/W4327525404","https://openalex.org/W2018580387","https://openalex.org/W4312269093","https://openalex.org/W3170261037","https://openalex.org/W4210420802","https://openalex.org/W2987568073","https://openalex.org/W2574301230"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"a":[3,20,75,84,106,118,126],"hybrid":[4],"model":[5,24,33,44,77,86],"is":[6,25,60,72,79,102,123],"proposed":[7],"to":[8,83,93,114],"simultaneously":[9],"mine":[10],"causal":[11,95],"connections":[12,96],"and":[13,34,97,131],"features":[14],"responsible":[15],"for":[16,52],"contemporaneous":[17,98],"correlations":[18],"in":[19],"multivariate":[21],"process.":[22],"The":[23,39,70,121],"developed":[26],"by":[27],"combining":[28],"the":[29,35,42,48,57,64,90],"vector":[30],"auto-regressive":[31],"exogenous":[32],"factor":[36],"analysis":[37],"model.":[38,58],"parameters":[40,91],"of":[41,108,128],"resulting":[43],"are":[45],"regularized":[46],"using":[47,117],"hierarchical":[49],"prior":[50],"distributions":[51],"pruning":[53],"insignificant/irrelevant":[54],"ones":[55],"from":[56,111],"It":[59],"then":[61,80],"estimated":[62],"under":[63],"variational":[65],"Bayesian":[66],"expectation":[67],"maximization":[68],"framework.":[69],"estimation":[71],"initiated":[73],"with":[74,125],"complex":[76,112],"which":[78],"systematically":[81],"reduced":[82],"simpler":[85,115],"that":[87],"retains":[88],"only":[89],"corresponding":[92],"significant":[94],"correlations.":[99],"Model":[100],"reduction":[101],"carried":[103],"out":[104],"through":[105],"series":[107],"deterministic":[109],"jumps":[110],"models":[113,116],"relevance":[119],"criterion.":[120],"approach":[122],"illustrated":[124],"number":[127],"simulated":[129],"examples":[130],"an":[132],"industrial":[133],"case":[134],"study.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
