{"id":"https://openalex.org/W2114102615","doi":"https://doi.org/10.5555/1953048.1953053","title":"Bayesian Generalized Kernel Mixed Models","display_name":"Bayesian Generalized Kernel Mixed Models","publication_year":2011,"publication_date":"2011-02-01","ids":{"openalex":"https://openalex.org/W2114102615","doi":"https://doi.org/10.5555/1953048.1953053","mag":"2114102615"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.294.749","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://people.ee.duke.edu/~lcarin/Kernel_mixed_models.pdf","raw_type":"text"},"type":"article","indexed_in":[],"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/A5100393616","display_name":"Zhihua Zhang","orcid":"https://orcid.org/0000-0003-3165-5213"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihua Zhang","raw_affiliation_strings":["Zhejiang, University"],"raw_orcid":"https://orcid.org/0000-0003-3165-5213","affiliations":[{"raw_affiliation_string":"Zhejiang, University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102152323","display_name":"Guang Dai","orcid":"https://orcid.org/0000-0002-3529-9087"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guang Dai","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3529-9087","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5049812527","display_name":"Michael I. Jordan","orcid":"https://orcid.org/0000-0001-8935-817X"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael I. Jordan","raw_affiliation_strings":["University of California, Berkeley"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Berkeley","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.1941,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.97279692,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"12","issue":"5","first_page":"111","last_page":"139"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9998000264167786,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9998000264167786,"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":0.9966999888420105,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.995199978351593,"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/kernel","display_name":"Kernel (algebra)","score":0.6276806592941284},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5529876351356506},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5454100966453552},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"Kernel regression","score":0.48290276527404785},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.47310709953308105},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4724814295768738},{"id":"https://openalex.org/keywords/reproducing-kernel-hilbert-space","display_name":"Reproducing kernel Hilbert space","score":0.4687989354133606},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.45248961448669434},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.44623619318008423},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4348503053188324},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4326450228691101},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4307910203933716},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.4181515574455261},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4168601632118225},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4161430597305298},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.39400169253349304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3664247989654541},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2702982723712921},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.23460456728935242},{"id":"https://openalex.org/keywords/hilbert-space","display_name":"Hilbert space","score":0.2155311405658722},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14922723174095154},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.1356578767299652}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6276806592941284},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5529876351356506},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5454100966453552},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.48290276527404785},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.47310709953308105},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4724814295768738},{"id":"https://openalex.org/C80884492","wikidata":"https://www.wikidata.org/wiki/Q3345678","display_name":"Reproducing kernel Hilbert space","level":3,"score":0.4687989354133606},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.45248961448669434},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.44623619318008423},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4348503053188324},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4326450228691101},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4307910203933716},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.4181515574455261},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4168601632118225},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4161430597305298},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39400169253349304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3664247989654541},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2702982723712921},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.23460456728935242},{"id":"https://openalex.org/C62799726","wikidata":"https://www.wikidata.org/wiki/Q190056","display_name":"Hilbert space","level":2,"score":0.2155311405658722},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14922723174095154},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.1356578767299652},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.294.749","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://people.ee.duke.edu/~lcarin/Kernel_mixed_models.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.414.3689","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.3689","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.zju.edu.cn/people/zhzhang/papers/zhang11a.pdf","raw_type":"text"},{"id":"mag:2114102615","is_oa":false,"landing_page_url":"http://ece.duke.edu/~lcarin/Kernel_mixed_models.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W171292237","https://openalex.org/W1486016120","https://openalex.org/W1528905581","https://openalex.org/W1540007258","https://openalex.org/W1554944419","https://openalex.org/W1567323455","https://openalex.org/W1571870753","https://openalex.org/W1596618679","https://openalex.org/W1648445109","https://openalex.org/W1746819321","https://openalex.org/W1934021597","https://openalex.org/W1982585616","https://openalex.org/W1982652137","https://openalex.org/W2010111480","https://openalex.org/W2010441486","https://openalex.org/W2020389170","https://openalex.org/W2024622153","https://openalex.org/W2025462409","https://openalex.org/W2031823405","https://openalex.org/W2041779820","https://openalex.org/W2043182541","https://openalex.org/W2051817458","https://openalex.org/W2057331441","https://openalex.org/W2098695576","https://openalex.org/W2099768828","https://openalex.org/W2100322632","https://openalex.org/W2106706098","https://openalex.org/W2108306139","https://openalex.org/W2112545207","https://openalex.org/W2114169935","https://openalex.org/W2116444583","https://openalex.org/W2117063635","https://openalex.org/W2123687908","https://openalex.org/W2135046866","https://openalex.org/W2137956165","https://openalex.org/W2142670722","https://openalex.org/W2146571341","https://openalex.org/W2146766088","https://openalex.org/W2148319469","https://openalex.org/W2148603752","https://openalex.org/W2164262940","https://openalex.org/W2166624680","https://openalex.org/W2185967851","https://openalex.org/W2338008992","https://openalex.org/W2488395402","https://openalex.org/W2781919434","https://openalex.org/W2963341722","https://openalex.org/W3148198191","https://openalex.org/W3214323364"],"related_works":["https://openalex.org/W1648445109","https://openalex.org/W165787321","https://openalex.org/W1480376833","https://openalex.org/W2253398379","https://openalex.org/W2148603752","https://openalex.org/W1560724230","https://openalex.org/W1540155273","https://openalex.org/W2164262940","https://openalex.org/W2144902422","https://openalex.org/W2108306139","https://openalex.org/W2100322632","https://openalex.org/W1746819321","https://openalex.org/W2481701519","https://openalex.org/W2769773494","https://openalex.org/W2059521293","https://openalex.org/W2135624048","https://openalex.org/W2129564505","https://openalex.org/W2884593909","https://openalex.org/W2004055202","https://openalex.org/W2151531457"],"abstract_inverted_index":{"We":[0,29],"propose":[1],"a":[2,26,31,34,45,54,87,106,130,143,154],"fully":[3],"Bayesian":[4,76,107],"methodology":[5,84],"for":[6,69,75,102,113,158],"generalized":[7,16,46],"kernel":[8,47,124],"mixed":[9,18],"models":[10,19],"(GKMMs),":[11],"which":[12,95],"are":[13],"extensions":[14],"of":[15,33,44,56,59,142],"linear":[17],"in":[20,94,121],"the":[21,41,57,60,96,117,122,139],"feature":[22,118],"space":[23,126],"induced":[24],"by":[25],"reproducing":[27,123],"kernel.":[28],"place":[30],"mixture":[32,51],"point-mass":[35],"distribution":[36],"and":[37,72,105],"Silverman\u2019s":[38],"g-prior":[39],"on":[40],"regression":[42,61],"vector":[43,62],"model":[48,103,108],"(GKM).":[49],"This":[50],"prior":[52],"allows":[53],"fraction":[55],"components":[58],"to":[63,85,138,153],"be":[64,136],"zero.":[65],"Thus,":[66,147],"it":[67],"serves":[68],"sparse":[70,149],"modeling":[71,150],"is":[73,100,111,127],"useful":[74],"computation.":[77],"In":[78],"particular,":[79],"we":[80],"exploit":[81],"data":[82],"augmentation":[83],"develop":[86],"Markov":[88],"chain":[89],"Monte":[90],"Carlo":[91],"(MCMC)":[92],"algorithm":[93],"reversible":[97],"jump":[98],"method":[99,110,157],"used":[101,112],"selection":[104],"averaging":[109],"posterior":[114],"prediction.":[115],"When":[116],"basis":[119],"expansion":[120,141],"Hilbert":[125],"treated":[128],"as":[129],"stochastic":[131],"process,":[132],"this":[133],"approach":[134],"can":[135],"related":[137],"Karhunen-Lo\u00e8ve":[140],"Gaussian":[144],"process":[145],"(GP).":[146],"our":[148],"framework":[151],"leads":[152],"flexible":[155],"approximation":[156],"GPs.":[159]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":5}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}