{"id":"https://openalex.org/W3187416000","doi":"https://doi.org/10.1080/10618600.2022.2050250","title":"The<i>G</i>-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models","display_name":"The<i>G</i>-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models","publication_year":2022,"publication_date":"2022-03-09","ids":{"openalex":"https://openalex.org/W3187416000","doi":"https://doi.org/10.1080/10618600.2022.2050250","mag":"3187416000"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2022.2050250","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2050250","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2108.01308","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065189850","display_name":"Willem van den Boom","orcid":"https://orcid.org/0000-0002-1777-3857"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Willem van den Boom","raw_affiliation_strings":["Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore;;"],"raw_orcid":"https://orcid.org/0000-0002-1777-3857","affiliations":[{"raw_affiliation_string":"Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore;;","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006951281","display_name":"Alexandros Beskos","orcid":"https://orcid.org/0000-0002-4577-8153"},"institutions":[{"id":"https://openalex.org/I4210128584","display_name":"The Alan Turing Institute","ror":"https://ror.org/035dkdb55","country_code":"GB","type":"facility","lineage":["https://openalex.org/I4210128584"]},{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alexandros Beskos","raw_affiliation_strings":["Alan Turing Institute, London, UK;;","Department of Statistical Science, University College London, London, UK;;"],"raw_orcid":"https://orcid.org/0000-0002-4577-8153","affiliations":[{"raw_affiliation_string":"Alan Turing Institute, London, UK;;","institution_ids":["https://openalex.org/I4210128584"]},{"raw_affiliation_string":"Department of Statistical Science, University College London, London, UK;;","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050004764","display_name":"Maria De Iorio","orcid":"https://orcid.org/0000-0003-3109-0478"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]},{"id":"https://openalex.org/I4210101200","display_name":"Singapore Institute for Clinical Sciences","ror":"https://ror.org/015p9va32","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I4210101200"]},{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB","SG"],"is_corresponding":false,"raw_author_name":"Maria De Iorio","raw_affiliation_strings":["Department of Statistical Science, University College London, London, UK;;","Singapore Institute for Clinical Sciences, A*STAR, Singapore, Singapore","Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore;;"],"raw_orcid":"https://orcid.org/0000-0003-3109-0478","affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London, UK;;","institution_ids":["https://openalex.org/I45129253"]},{"raw_affiliation_string":"Singapore Institute for Clinical Sciences, A*STAR, Singapore, Singapore","institution_ids":["https://openalex.org/I4210101200","https://openalex.org/I115228651"]},{"raw_affiliation_string":"Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore;;","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065189850"],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":2.7124,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.905696,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"31","issue":"4","first_page":"1215","last_page":"1224"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9979000091552734,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9979000091552734,"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":0.9977999925613403,"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/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8825210332870483},{"id":"https://openalex.org/keywords/wishart-distribution","display_name":"Wishart distribution","score":0.8232284784317017},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.7547457218170166},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.6336708664894104},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.558158814907074},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.508366048336029},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48175716400146484},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.4649926722049713},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4534884989261627},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43636494874954224},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43163707852363586},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.395987868309021},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2614741921424866},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14161577820777893},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.07323956489562988}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8825210332870483},{"id":"https://openalex.org/C33962027","wikidata":"https://www.wikidata.org/wiki/Q1930697","display_name":"Wishart distribution","level":3,"score":0.8232284784317017},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.7547457218170166},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.6336708664894104},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.558158814907074},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.508366048336029},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48175716400146484},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.4649926722049713},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4534884989261627},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43636494874954224},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43163707852363586},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.395987868309021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2614741921424866},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14161577820777893},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.07323956489562988}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/10618600.2022.2050250","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2050250","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2108.01308","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.01308","pdf_url":"https://arxiv.org/pdf/2108.01308","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2108.01308","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.01308","pdf_url":"https://arxiv.org/pdf/2108.01308","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":66,"referenced_works":["https://openalex.org/W143236119","https://openalex.org/W1533758202","https://openalex.org/W1905223283","https://openalex.org/W1967046553","https://openalex.org/W1979115894","https://openalex.org/W1984048068","https://openalex.org/W1988399610","https://openalex.org/W2001161274","https://openalex.org/W2008561984","https://openalex.org/W2008631631","https://openalex.org/W2011471859","https://openalex.org/W2012328630","https://openalex.org/W2015226626","https://openalex.org/W2015590129","https://openalex.org/W2019963883","https://openalex.org/W2026097090","https://openalex.org/W2038816913","https://openalex.org/W2042470150","https://openalex.org/W2042770989","https://openalex.org/W2051264056","https://openalex.org/W2055841987","https://openalex.org/W2056720303","https://openalex.org/W2065981470","https://openalex.org/W2068558450","https://openalex.org/W2071628652","https://openalex.org/W2072082202","https://openalex.org/W2074329608","https://openalex.org/W2095067466","https://openalex.org/W2099978770","https://openalex.org/W2100270478","https://openalex.org/W2106706098","https://openalex.org/W2112087830","https://openalex.org/W2120672356","https://openalex.org/W2124227444","https://openalex.org/W2132555912","https://openalex.org/W2136796925","https://openalex.org/W2163413340","https://openalex.org/W2167943787","https://openalex.org/W2597289420","https://openalex.org/W2605710229","https://openalex.org/W2612512107","https://openalex.org/W2624753617","https://openalex.org/W2802528245","https://openalex.org/W2920804790","https://openalex.org/W2951779732","https://openalex.org/W2962997929","https://openalex.org/W2963026254","https://openalex.org/W2963145051","https://openalex.org/W2963330637","https://openalex.org/W2964033684","https://openalex.org/W3099870158","https://openalex.org/W3100089080","https://openalex.org/W3101022954","https://openalex.org/W3101226854","https://openalex.org/W3101886251","https://openalex.org/W3106284700","https://openalex.org/W3110540550","https://openalex.org/W3126123762","https://openalex.org/W3135464130","https://openalex.org/W3153760897","https://openalex.org/W3185747415","https://openalex.org/W4238253035","https://openalex.org/W4247985008","https://openalex.org/W4287203604","https://openalex.org/W4388323202","https://openalex.org/W4399524164"],"related_works":["https://openalex.org/W3125971950","https://openalex.org/W1580681286","https://openalex.org/W2116700007","https://openalex.org/W2175355783","https://openalex.org/W2622204791","https://openalex.org/W1579866848","https://openalex.org/W2066716418","https://openalex.org/W2905524938","https://openalex.org/W1546022168","https://openalex.org/W3198356641"],"abstract_inverted_index":{"Gaussian":[0],"graphical":[1],"models":[2],"can":[3],"capture":[4],"complex":[5],"dependency":[6],"structures":[7],"among":[8],"variables.":[9],"For":[10],"such":[11],"models,":[12],"Bayesian":[13],"inference":[14,145],"is":[15,47],"attractive":[16],"as":[17],"it":[18],"provides":[19,138],"principled":[20],"ways":[21],"to":[22,27,99],"incorporate":[23],"prior":[24,41],"information":[25],"and":[26,84,124,132],"quantify":[28],"uncertainty":[29],"through":[30],"the":[31,38,44,65,81,90,104,107,112,154],"posterior":[32,35,144],"distribution.":[33,114],"However,":[34],"computation":[36],"under":[37],"conjugate":[39],"G-Wishart":[40,66,113],"distribution":[42,88],"on":[43,89,130],"precision":[45,82],"matrix":[46,83],"expensive":[48,109],"for":[49,80,143,153],"general":[50],"nondecomposable":[51],"graphs.":[52],"We":[53],"therefore":[54],"propose":[55],"a":[56,139],"new":[57],"Markov":[58],"chain":[59],"Monte":[60],"Carlo":[61],"(MCMC)":[62],"method":[63],"named":[64],"weighted":[67],"proposal":[68,87],"algorithm":[69],"(WWA).":[70],"WWA\u2019s":[71],"distinctive":[72],"features":[73],"include":[74],"delayed":[75],"acceptance":[76],"MCMC,":[77],"Gibbs":[78],"updates":[79],"an":[85],"informed":[86],"graph":[91],"space":[92],"that":[93,136],"enables":[94],"embarrassingly":[95],"parallel":[96],"computations.":[97],"Compared":[98],"existing":[100],"approaches,":[101],"WWA":[102,137],"reduces":[103],"frequency":[105],"of":[106],"relatively":[108],"sampling":[110],"from":[111],"This":[115],"results":[116],"in":[117],"faster":[118],"MCMC":[119,122,149],"convergence,":[120],"improved":[121],"mixing":[123],"reduced":[125],"computing":[126],"time.":[127],"Numerical":[128],"studies":[129],"simulated":[131],"real":[133],"data":[134],"show":[135],"more":[140],"efficient":[141],"tool":[142],"than":[146],"competing":[147],"state-of-the-art":[148],"algorithms.":[150],"Supplemental":[151],"materials":[152],"article":[155],"are":[156],"available":[157],"online.":[158]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2021-08-16T00:00:00"}