{"id":"https://openalex.org/W4386772321","doi":"https://doi.org/10.1080/10618600.2023.2258186","title":"Gibbs Sampler for Matrix Generalized Inverse Gaussian Distributions","display_name":"Gibbs Sampler for Matrix Generalized Inverse Gaussian Distributions","publication_year":2023,"publication_date":"2023-09-15","ids":{"openalex":"https://openalex.org/W4386772321","doi":"https://doi.org/10.1080/10618600.2023.2258186"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2023.2258186","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1080/10618600.2023.2258186","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","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"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":["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/A5086498988","display_name":"Yasuyuki Hamura","orcid":null},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yasuyuki Hamura","raw_affiliation_strings":["Graduate School of Economics, Kyoto University, Sakyo, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Economics, Kyoto University, Sakyo, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076831167","display_name":"Kaoru Irie","orcid":null},"institutions":[{"id":"https://openalex.org/I153327471","display_name":"Bunkyo University","ror":"https://ror.org/053h75930","country_code":"JP","type":"education","lineage":["https://openalex.org/I153327471"]},{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kaoru Irie","raw_affiliation_strings":["Faculty of Economics, The University of Tokyo, Bunkyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Economics, The University of Tokyo, Bunkyo, Tokyo, Japan","institution_ids":["https://openalex.org/I153327471","https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054166002","display_name":"Shonosuke Sugasawa","orcid":"https://orcid.org/0000-0002-9495-4280"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shonosuke Sugasawa","raw_affiliation_strings":["Faculty of Economics, Keio University, Minato, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Economics, Keio University, Minato, Tokyo, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5086498988"],"corresponding_institution_ids":["https://openalex.org/I22299242"],"apc_list":null,"apc_paid":null,"fwci":0.2555,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63921442,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"33","issue":"2","first_page":"331","last_page":"340"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.984499990940094,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.984499990940094,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9714000225067139,"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/T13487","display_name":"Statistical and numerical algorithms","score":0.964900016784668,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/gibbs-sampling","display_name":"Gibbs sampling","score":0.6807548999786377},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.533073902130127},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.49323123693466187},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.4843696057796478},{"id":"https://openalex.org/keywords/inverse-gaussian-distribution","display_name":"Inverse Gaussian distribution","score":0.4737620949745178},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4580153524875641},{"id":"https://openalex.org/keywords/generalized-inverse-gaussian-distribution","display_name":"Generalized inverse Gaussian distribution","score":0.4511064291000366},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.44780856370925903},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.4030598998069763},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4013885259628296},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.39492836594581604},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2734754681587219},{"id":"https://openalex.org/keywords/gaussian-random-field","display_name":"Gaussian random field","score":0.21868014335632324},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.19286233186721802},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.19060200452804565},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.17479881644248962},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.09838053584098816},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.09067836403846741},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.0872812271118164}],"concepts":[{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6807548999786377},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.533073902130127},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.49323123693466187},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.4843696057796478},{"id":"https://openalex.org/C132878287","wikidata":"https://www.wikidata.org/wiki/Q1671727","display_name":"Inverse Gaussian distribution","level":3,"score":0.4737620949745178},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4580153524875641},{"id":"https://openalex.org/C18912344","wikidata":"https://www.wikidata.org/wiki/Q866810","display_name":"Generalized inverse Gaussian distribution","level":5,"score":0.4511064291000366},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.44780856370925903},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.4030598998069763},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4013885259628296},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.39492836594581604},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2734754681587219},{"id":"https://openalex.org/C51267290","wikidata":"https://www.wikidata.org/wiki/Q5527848","display_name":"Gaussian random field","level":4,"score":0.21868014335632324},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.19286233186721802},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.19060200452804565},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.17479881644248962},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.09838053584098816},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.09067836403846741},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0872812271118164},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2023.2258186","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1080/10618600.2023.2258186","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","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"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"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5899999737739563}],"awards":[{"id":"https://openalex.org/G4467227487","display_name":"Bayesian spatio-temporal modeling of income inequality","funder_award_id":"20H00080","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G6693740113","display_name":"\u30ed\u30d0\u30b9\u30c8\u7d71\u8a08\u30fb\u7e2e\u5c0f\u63a8\u5b9a\u306b\u6709\u7528\u306a\u65b0\u3057\u3044\u78ba\u7387\u5206\u5e03\u65cf\u306e\u7814\u7a76","funder_award_id":"22K13374","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G6812787663","display_name":"Advances in Bayesian Modeling for Large-Scale Data","funder_award_id":"23K20592","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7824458385","display_name":"Research on methods for effectively combining information from multidimensional data","funder_award_id":"22K20132","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W41179353","https://openalex.org/W1970431297","https://openalex.org/W1977691943","https://openalex.org/W1981432875","https://openalex.org/W2007306748","https://openalex.org/W2058150240","https://openalex.org/W2099604794","https://openalex.org/W2127874106","https://openalex.org/W2131668296","https://openalex.org/W2140835403","https://openalex.org/W2769172681","https://openalex.org/W2950411888","https://openalex.org/W2963691001","https://openalex.org/W2991266753","https://openalex.org/W3022791820","https://openalex.org/W3121581836","https://openalex.org/W3165126802","https://openalex.org/W4292403327","https://openalex.org/W6674119525"],"related_works":["https://openalex.org/W1968259981","https://openalex.org/W2022574511","https://openalex.org/W1545728499","https://openalex.org/W115602763","https://openalex.org/W4309956458","https://openalex.org/W2996035120","https://openalex.org/W2092366338","https://openalex.org/W1978122500","https://openalex.org/W3180753773","https://openalex.org/W122958856"],"abstract_inverted_index":{"Sampling":[0],"from":[1],"matrix":[2],"generalized":[3,72],"inverse":[4,73],"Gaussian":[5,74],"(MGIG)":[6],"distributions":[7,31,48],"is":[8],"required":[9],"in":[10,105],"Markov":[11],"chain":[12],"Monte":[13],"Carlo":[14],"(MCMC)":[15],"algorithms":[16],"for":[17,28,45,90,126],"a":[18,40],"variety":[19],"of":[20,60,63,82,113],"statistical":[21],"models.":[22],"However,":[23],"an":[24],"efficient":[25],"sampling":[26],"scheme":[27],"the":[29,46,51,57,61,64,83,98,110,114],"MGIG":[30,47],"has":[32],"not":[33],"been":[34],"fully":[35],"developed.":[36],"We":[37,54,108],"here":[38],"propose":[39],"novel":[41],"blocked":[42],"Gibbs":[43,116],"sampler":[44,117],"based":[49],"on":[50],"Cholesky":[52],"decomposition.":[53],"show":[55],"that":[56,97],"full":[58],"conditionals":[59],"entries":[62],"diagonal":[65],"and":[66,75,121],"unit":[67],"lower-triangular":[68],"matrices":[69],"are":[70,129],"univariate":[71],"multivariate":[76],"normal":[77],"distributions,":[78],"respectively.":[79],"Several":[80],"variants":[81],"Metropolis-Hastings":[84],"algorithm":[85],"can":[86],"also":[87],"be":[88],"considered":[89],"this":[91,127],"problem,":[92],"but":[93],"we":[94],"mathematically":[95],"prove":[96],"average":[99],"acceptance":[100],"rates":[101],"become":[102],"extremely":[103],"low":[104],"particular":[106],"scenarios.":[107],"demonstrate":[109],"computational":[111],"efficiency":[112],"proposed":[115],"through":[118],"simulation":[119],"studies":[120],"data":[122],"analysis.":[123],"Supplementary":[124],"materials":[125],"article":[128],"available":[130],"online.":[131]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-15T07:11:24.734988","created_date":"2025-10-10T00:00:00"}