{"id":"https://openalex.org/W2574367259","doi":"https://doi.org/10.3233/978-1-61499-672-9-82","title":"Student-t Process Regression with Dependent Student-t Noise","display_name":"Student-t Process Regression with Dependent Student-t Noise","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2574367259","doi":"https://doi.org/10.3233/978-1-61499-672-9-82","mag":"2574367259"},"language":"en","primary_location":{"id":"doi:10.3233/978-1-61499-672-9-82","is_oa":true,"landing_page_url":"https://doi.org/10.3233/978-1-61499-672-9-82","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/978-1-61499-672-9-82","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059534557","display_name":"Qingtao Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tang Qingtao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004999983","display_name":"Yisen Wang","orcid":"https://orcid.org/0000-0002-1612-4433"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Yisen","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034104790","display_name":"Shu\u2010Tao Xia","orcid":"https://orcid.org/0000-0002-8639-982X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Shu-Tao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5059534557"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.424,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.94517596,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9728999733924866,"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":0.9728999733924866,"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/T10320","display_name":"Neural Networks and Applications","score":0.9697999954223633,"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/T10791","display_name":"Advanced Control Systems Optimization","score":0.9477999806404114,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4792311489582062},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44233325123786926},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4259498715400696},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4209224581718445},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40065133571624756},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.38013607263565063},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.36271148920059204},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3129953444004059},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24607941508293152},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.06164243817329407}],"concepts":[{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4792311489582062},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44233325123786926},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4259498715400696},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4209224581718445},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40065133571624756},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.38013607263565063},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.36271148920059204},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3129953444004059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24607941508293152},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.06164243817329407},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/978-1-61499-672-9-82","is_oa":true,"landing_page_url":"https://doi.org/10.3233/978-1-61499-672-9-82","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/978-1-61499-672-9-82","is_oa":true,"landing_page_url":"https://doi.org/10.3233/978-1-61499-672-9-82","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4381136829","https://openalex.org/W31220157","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W3186837933","https://openalex.org/W2368989808","https://openalex.org/W1969346022","https://openalex.org/W2034959125"],"abstract_inverted_index":{"Gaussian":[0,28],"Process":[1,46,77],"Regression":[2,47,78],"(GPR)":[3],"is":[4,88,122],"a":[5,31,44,71,89,109,157,164],"powerful":[6],"non-parametric":[7],"method.":[8],"However,":[9],"GPR":[10],"may":[11],"perform":[12],"poorly":[13],"if":[14],"the":[15,23,27,58,75,80,93,100,106,114,125,131,143,148,172,177,192],"data":[16],"are":[17,66,195],"contaminated":[18],"by":[19],"outliers.":[20],"To":[21],"address":[22],"issue,":[24],"we":[25,98],"replace":[26],"process":[29,33,95],"with":[30,48,79,163],"Student-t":[32,37,45,50,76,94,132],"and":[34,61,139],"introduce":[35],"dependent":[36],"noise":[38,51,81],"in":[39],"this":[40],"paper,":[41],"leading":[42],"to":[43,74],"Dependent":[49],"model":[52],"(TPRD).":[53],"Closed":[54],"form":[55],"expressions":[56],"for":[57,92],"marginal":[59,116],"likelihood":[60,117],"predictive":[62,145],"distribution":[63],"of":[64,102,119,124,127,130,150],"TPRD":[65,69,138,162,179],"derived.":[67],"Besides,":[68],"gives":[70],"probabilistic":[72],"interpretation":[73],"incorporated":[82],"into":[83],"its":[84],"Kernel":[85],"(TPRK),":[86],"which":[87,134],"common":[90],"approach":[91],"regression.":[96],"Moreover,":[97],"analyze":[99],"influence":[101],"different":[103],"kernels.":[104],"If":[105,171],"kernel":[107,173],"meets":[108],"condition,":[110],"called":[111],"β-property":[112],"here,":[113],"maximum":[115],"estimation":[118],"TPRD's":[120],"hyperparameters":[121],"independent":[123],"degrees":[126,149],"freedom":[128,151],"ν":[129,152,166],"process,":[133],"implies":[135],"that":[136,161],"GPR,":[137,184],"TPRK":[140],"have":[141,176],"exactly":[142],"same":[144],"mean.":[146],"Empirically,":[147],"could":[153],"be":[154],"regarded":[155],"as":[156],"convergence":[158],"accelerator,":[159],"indicating":[160],"suitable":[165],"performs":[167],"faster":[168],"than":[169,183],"GPR.":[170],"does":[174],"not":[175],"β-property,":[178],"has":[180],"better":[181],"performances":[182],"without":[185],"additional":[186],"computational":[187],"cost.":[188],"On":[189],"benchmark":[190],"datasets,":[191],"proposed":[193],"results":[194],"verified.":[196]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2017-01-26T00:00:00"}