{"id":"https://openalex.org/W4376604137","doi":"https://doi.org/10.1162/neco_a_01592","title":"Posterior Covariance Information Criterion for Weighted Inference","display_name":"Posterior Covariance Information Criterion for Weighted Inference","publication_year":2023,"publication_date":"2023-05-15","ids":{"openalex":"https://openalex.org/W4376604137","doi":"https://doi.org/10.1162/neco_a_01592","pmid":"https://pubmed.ncbi.nlm.nih.gov/37187166"},"language":"en","primary_location":{"id":"doi:10.1162/neco_a_01592","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01592","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","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/A5077898178","display_name":"Yukito Iba","orcid":null},"institutions":[{"id":"https://openalex.org/I4210134673","display_name":"The Institute of Statistical Mathematics","ror":"https://ror.org/03jcejr58","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I4210134673","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yukito Iba","raw_affiliation_strings":["The Institute of Statistical Mathematics, Tokyo 190-8562, Japan iba@ism.ac.jp"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Institute of Statistical Mathematics, Tokyo 190-8562, Japan iba@ism.ac.jp","institution_ids":["https://openalex.org/I4210134673"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026928457","display_name":"Keisuke Yano","orcid":"https://orcid.org/0000-0003-0417-3104"},"institutions":[{"id":"https://openalex.org/I4210134673","display_name":"The Institute of Statistical Mathematics","ror":"https://ror.org/03jcejr58","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I4210134673","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Keisuke Yano","raw_affiliation_strings":["The Institute of Statistical Mathematics, Tokyo 190-8562, Japan yano@ism.ac.jp"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Institute of Statistical Mathematics, Tokyo 190-8562, Japan yano@ism.ac.jp","institution_ids":["https://openalex.org/I4210134673"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5026928457","https://openalex.org/A5077898178"],"corresponding_institution_ids":["https://openalex.org/I4210134673"],"apc_list":null,"apc_paid":null,"fwci":1.0066,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75987285,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"35","issue":"7","first_page":"1340","last_page":"1361"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9991000294685364,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9991000294685364,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9991000294685364,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9962000250816345,"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/covariance","display_name":"Covariance","score":0.6301329731941223},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6193847060203552},{"id":"https://openalex.org/keywords/information-criteria","display_name":"Information Criteria","score":0.5152538418769836},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.5044068098068237},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.47541600465774536},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.46966752409935},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45163920521736145},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4356961250305176},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4183339476585388},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4159417450428009},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38983413577079773},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.37718597054481506},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3438943028450012},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2694883644580841},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.195492684841156}],"concepts":[{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6301329731941223},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6193847060203552},{"id":"https://openalex.org/C2776709221","wikidata":"https://www.wikidata.org/wiki/Q6031040","display_name":"Information Criteria","level":3,"score":0.5152538418769836},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5044068098068237},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.47541600465774536},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.46966752409935},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45163920521736145},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4356961250305176},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4183339476585388},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4159417450428009},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38983413577079773},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37718597054481506},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3438943028450012},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2694883644580841},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.195492684841156},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1162/neco_a_01592","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01592","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},{"id":"pmid:37187166","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37187166","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":"Neural computation","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2394134375","display_name":"Information Criteria for Various Predictive Settings: A Computational Approach","funder_award_id":"21K12067","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5040970261","display_name":null,"funder_award_id":"21H05205","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G6369241799","display_name":"Construction of estimation methods and their uncertainty quantification methods for high-dimensional count data focusing on structural constraints","funder_award_id":"19K20222","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7255015823","display_name":"Uncertainty evaluation and prediction of spatio-temporal data","funder_award_id":"23K11024","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W189742998","https://openalex.org/W1502338185","https://openalex.org/W1922851081","https://openalex.org/W2009187570","https://openalex.org/W2034368206","https://openalex.org/W2049213105","https://openalex.org/W2054640142","https://openalex.org/W2055505622","https://openalex.org/W2057765075","https://openalex.org/W2081724349","https://openalex.org/W2087373687","https://openalex.org/W2097079088","https://openalex.org/W2123222757","https://openalex.org/W2133519275","https://openalex.org/W2150291618","https://openalex.org/W2203714058","https://openalex.org/W2243651912","https://openalex.org/W2292751606","https://openalex.org/W2413397702","https://openalex.org/W2589196999","https://openalex.org/W2896426532","https://openalex.org/W3121290475","https://openalex.org/W3123421154","https://openalex.org/W4248681815","https://openalex.org/W4399574326","https://openalex.org/W6607672814","https://openalex.org/W6665272994","https://openalex.org/W6678521640"],"related_works":["https://openalex.org/W2027276540","https://openalex.org/W4205763938","https://openalex.org/W2366775747","https://openalex.org/W3168765527","https://openalex.org/W4318483226","https://openalex.org/W3121470121","https://openalex.org/W3152325287","https://openalex.org/W1995851699","https://openalex.org/W2571640350","https://openalex.org/W154616223"],"abstract_inverted_index":{"For":[0],"predictive":[1,32],"evaluation":[2,41],"based":[3],"on":[4],"quasi-posterior":[5],"distributions,":[6],"we":[7,90,99],"develop":[8],"a":[9,71],"new":[10],"information":[11,16,24],"criterion,":[12],"the":[13,21,37,40,43,55,107],"posterior":[14,72],"covariance":[15,73],"criterion":[17,25,69],"(PCIC).":[18],"PCIC":[19,93,102],"generalizes":[20],"widely":[22],"applicable":[23],"(WAIC)":[26],"so":[27],"as":[28],"to":[29,106],"effectively":[30],"handle":[31],"scenarios":[33,53],"where":[34],"likelihoods":[35],"for":[36],"estimation":[38],"and":[39,64,75,120],"of":[42,51],"model":[44],"may":[45],"be":[46],"different.":[47],"A":[48],"typical":[49],"example":[50],"such":[52],"is":[54,76,103],"weighted":[56,115],"likelihood":[57],"inference,":[58],"including":[59],"prediction":[60],"under":[61,111],"covariate":[62],"shift":[63],"counterfactual":[65],"prediction.":[66],"The":[67],"proposed":[68],"uses":[70],"form":[74],"computed":[77],"by":[78],"using":[79],"only":[80],"one":[81],"Markov":[82],"chain":[83],"Monte":[84],"Carlo":[85],"run.":[86],"Through":[87],"numerical":[88],"examples,":[89],"demonstrate":[91],"how":[92],"can":[94],"apply":[95],"in":[96,114],"practice.":[97],"Further,":[98],"show":[100],"that":[101],"asymptotically":[104],"unbiased":[105],"quasi-Bayesian":[108],"generalization":[109],"error":[110],"mild":[112],"conditions":[113],"inference":[116],"with":[117],"both":[118],"regular":[119],"singular":[121],"statistical":[122],"models.":[123]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}