{"id":"https://openalex.org/W3138317451","doi":"https://doi.org/10.1080/10618600.2022.2099403","title":"Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models","display_name":"Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models","publication_year":2022,"publication_date":"2022-07-08","ids":{"openalex":"https://openalex.org/W3138317451","doi":"https://doi.org/10.1080/10618600.2022.2099403","mag":"3138317451"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2022.2099403","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2099403","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/2103.07425","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078923157","display_name":"Alex Stringer","orcid":"https://orcid.org/0000-0002-4133-6884"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Alex Stringer","raw_affiliation_strings":["Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada;"],"raw_orcid":"https://orcid.org/0000-0002-4133-6884","affiliations":[{"raw_affiliation_string":"Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada;","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042275901","display_name":"Patrick Brown","orcid":"https://orcid.org/0000-0003-2541-3744"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]},{"id":"https://openalex.org/I4210137763","display_name":"Centre for Global Health Research","ror":"https://ror.org/03xasj370","country_code":"CA","type":"nonprofit","lineage":["https://openalex.org/I4210137763"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Patrick Brown","raw_affiliation_strings":["Department of Statistical Sciences, University of Toronto, Centre for Global Health Research, Toronto, Ontario, Canada;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistical Sciences, University of Toronto, Centre for Global Health Research, Toronto, Ontario, Canada;","institution_ids":["https://openalex.org/I185261750","https://openalex.org/I4210137763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101973233","display_name":"James E. Stafford","orcid":"https://orcid.org/0000-0001-8777-9704"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jamie Stafford","raw_affiliation_strings":["Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada","institution_ids":["https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5078923157"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":3.0681,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.92517089,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"32","issue":"1","first_page":"84","last_page":"98"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9994000196456909,"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":0.9994000196456909,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9987999796867371,"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":0.9948999881744385,"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/laplaces-method","display_name":"Laplace's method","score":0.8497457504272461},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5397509336471558},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5343396663665771},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.51212477684021},{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.5049715638160706},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4970732033252716},{"id":"https://openalex.org/keywords/cox-process","display_name":"Cox process","score":0.4791692793369293},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.47670549154281616},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4276537001132965},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4239787459373474},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.42065536975860596},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.41968512535095215},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.41730672121047974},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4110843241214752},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.3493114113807678},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3308471441268921},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.20281606912612915},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17041927576065063}],"concepts":[{"id":"https://openalex.org/C22243797","wikidata":"https://www.wikidata.org/wiki/Q2058297","display_name":"Laplace's method","level":3,"score":0.8497457504272461},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5397509336471558},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5343396663665771},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.51212477684021},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.5049715638160706},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4970732033252716},{"id":"https://openalex.org/C155051063","wikidata":"https://www.wikidata.org/wiki/Q1290919","display_name":"Cox process","level":4,"score":0.4791692793369293},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.47670549154281616},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4276537001132965},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4239787459373474},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.42065536975860596},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.41968512535095215},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.41730672121047974},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4110843241214752},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3493114113807678},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3308471441268921},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.20281606912612915},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17041927576065063},{"id":"https://openalex.org/C166144826","wikidata":"https://www.wikidata.org/wiki/Q1145117","display_name":"Poisson process","level":3,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/10618600.2022.2099403","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2099403","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:2103.07425","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.07425","pdf_url":"https://arxiv.org/pdf/2103.07425","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:2103.07425","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.07425","pdf_url":"https://arxiv.org/pdf/2103.07425","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":[{"id":"https://metadata.un.org/sdg/3","score":0.8299999833106995,"display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W103475893","https://openalex.org/W1460189015","https://openalex.org/W1487139063","https://openalex.org/W1743110662","https://openalex.org/W1747046542","https://openalex.org/W1790462712","https://openalex.org/W1803970550","https://openalex.org/W1818484123","https://openalex.org/W1837874438","https://openalex.org/W1860392020","https://openalex.org/W1967495697","https://openalex.org/W1980669971","https://openalex.org/W2010111476","https://openalex.org/W2014581807","https://openalex.org/W2017696952","https://openalex.org/W2034878612","https://openalex.org/W2040635790","https://openalex.org/W2041722960","https://openalex.org/W2047330460","https://openalex.org/W2058753770","https://openalex.org/W2102810005","https://openalex.org/W2108668442","https://openalex.org/W2111188590","https://openalex.org/W2115839151","https://openalex.org/W2119160928","https://openalex.org/W2123871098","https://openalex.org/W2144898279","https://openalex.org/W2149174640","https://openalex.org/W2149221460","https://openalex.org/W2151535668","https://openalex.org/W2165429502","https://openalex.org/W2166414779","https://openalex.org/W2167943787","https://openalex.org/W2203714058","https://openalex.org/W2225156818","https://openalex.org/W2340515955","https://openalex.org/W2407194592","https://openalex.org/W2518496494","https://openalex.org/W2522337348","https://openalex.org/W2577537660","https://openalex.org/W2593643174","https://openalex.org/W2604738573","https://openalex.org/W2620778781","https://openalex.org/W2803315673","https://openalex.org/W2893051096","https://openalex.org/W2897692455","https://openalex.org/W2913668833","https://openalex.org/W2924323180","https://openalex.org/W2963282124","https://openalex.org/W2963505885","https://openalex.org/W2971924430","https://openalex.org/W3006568072","https://openalex.org/W3043464877","https://openalex.org/W3099809709","https://openalex.org/W3102810253","https://openalex.org/W3103114896","https://openalex.org/W3103300580","https://openalex.org/W3105163611","https://openalex.org/W3123421154","https://openalex.org/W3127649592","https://openalex.org/W3141322579","https://openalex.org/W4242630960","https://openalex.org/W4285719527","https://openalex.org/W6636166754"],"related_works":["https://openalex.org/W2109606780","https://openalex.org/W1480082381","https://openalex.org/W4299976848","https://openalex.org/W308423894","https://openalex.org/W1436597729","https://openalex.org/W4206042338","https://openalex.org/W3084023536","https://openalex.org/W2952102841","https://openalex.org/W2613020357","https://openalex.org/W2574486962"],"abstract_inverted_index":{"We":[0,85,130],"define":[1],"a":[2,16,36,66,153,158],"novel":[3],"class":[4,34,75],"of":[5,19,39,57,138,166,175,180],"additive":[6,27],"models,":[7,49],"called":[8],"Extended":[9],"Latent":[10],"Gaussian":[11],"Models,":[12],"that":[13,87,104],"allow":[14],"for":[15,73],"wide":[17],"range":[18,38],"response":[20],"distributions":[21],"and":[22,29,50,81,99,109,122,162,178],"flexible":[23],"relationships":[24],"between":[25],"the":[26,58,88,132,136,164,167,176],"predictor":[28],"mean":[30],"response.":[31],"The":[32],"new":[33,133],"covers":[35],"broad":[37],"interesting":[40],"models":[41],"including":[42],"multi-resolution":[43],"spatial":[44,160],"processes,":[45],"partial":[46],"likelihood-based":[47],"survival":[48,150],"multivariate":[51,173],"measurement":[52],"error":[53,89],"models.":[54],"Because":[55],"computation":[56],"exact":[59],"posterior":[60],"distribution":[61],"is":[62,94],"infeasible,":[63],"we":[64],"develop":[65],"fast,":[67],"scalable":[68],"approximate":[69,92],"Bayesian":[70],"inference":[71],"methodology":[72],"this":[74],"based":[76,116],"on":[77,117],"nested":[78],"Gaussian,":[79],"Laplace,":[80],"adaptive":[82],"quadrature":[83],"approximations.":[84],"prove":[86],"in":[90,142,183],"these":[91],"posteriors":[93],"op(1)":[95],"under":[96],"standard":[97],"conditions,":[98],"provide":[100],"numerical":[101],"evidence":[102],"suggesting":[103],"our":[105],"method":[106,134],"runs":[107],"faster":[108],"scales":[110],"to":[111,135],"larger":[112],"datasets":[113],"than":[114],"methods":[115],"Integrated":[118],"Nested":[119],"Laplace":[120],"Approximations":[121],"Markov":[123],"Chain":[124],"Monte":[125],"Carlo,":[126],"with":[127,157],"comparable":[128],"accuracy.":[129],"apply":[131],"mapping":[137,148],"malaria":[139],"incidence":[140],"rates":[141],"continuous":[143],"space":[144],"using":[145,152,171],"aggregated":[146],"data,":[147],"leukaemia":[149],"hazards":[151],"Cox":[154],"Proportional-Hazards":[155],"model":[156],"continuously-varying":[159],"process,":[161],"estimating":[163],"mass":[165],"Milky":[168],"Way":[169],"Galaxy":[170],"noisy":[172],"measurements":[174],"positions":[177],"velocities":[179],"star":[181],"clusters":[182],"its":[184],"orbit.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}