{"id":"https://openalex.org/W3130280819","doi":"https://doi.org/10.1137/20m1320432","title":"Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence","display_name":"Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3130280819","doi":"https://doi.org/10.1137/20m1320432","mag":"3130280819"},"language":"en","primary_location":{"id":"doi:10.1137/20m1320432","is_oa":false,"landing_page_url":"https://doi.org/10.1137/20m1320432","pdf_url":null,"source":{"id":"https://openalex.org/S2911293512","display_name":"SIAM/ASA Journal on Uncertainty Quantification","issn_l":"2166-2525","issn":["2166-2525"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM/ASA Journal on Uncertainty Quantification","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/A5059311361","display_name":"Michael Sinsbeck","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Michael Sinsbeck","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010900944","display_name":"Emily Cooke","orcid":"https://orcid.org/0000-0002-6808-009X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emily Cooke","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5091354130","display_name":"Wolfgang Nowak","orcid":"https://orcid.org/0000-0003-2583-8865"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wolfgang Nowak","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5059311361"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5919,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.70053934,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"9","issue":"1","first_page":"260","last_page":"279"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"Marginal likelihood","score":0.7726171016693115},{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.7451041340827942},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6490272283554077},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.6010556221008301},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5827844142913818},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5811417698860168},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5292258262634277},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.4970562756061554},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.4778965711593628},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4718458652496338},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.468948096036911},{"id":"https://openalex.org/keywords/parameter-space","display_name":"Parameter space","score":0.4386802315711975},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4170878529548645},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.40410664677619934},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3859763741493225},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34954923391342163},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3494197726249695},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28991031646728516},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2528379559516907}],"concepts":[{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.7726171016693115},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.7451041340827942},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6490272283554077},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.6010556221008301},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5827844142913818},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5811417698860168},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5292258262634277},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.4970562756061554},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4778965711593628},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4718458652496338},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.468948096036911},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.4386802315711975},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4170878529548645},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.40410664677619934},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3859763741493225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34954923391342163},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3494197726249695},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28991031646728516},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2528379559516907},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"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":1,"locations":[{"id":"doi:10.1137/20m1320432","is_oa":false,"landing_page_url":"https://doi.org/10.1137/20m1320432","pdf_url":null,"source":{"id":"https://openalex.org/S2911293512","display_name":"SIAM/ASA Journal on Uncertainty Quantification","issn_l":"2166-2525","issn":["2166-2525"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM/ASA Journal on Uncertainty Quantification","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2671487527","display_name":null,"funder_award_id":"NO 805/8-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1488022545","https://openalex.org/W1510052597","https://openalex.org/W1529206345","https://openalex.org/W1603903339","https://openalex.org/W1693986406","https://openalex.org/W1965555277","https://openalex.org/W1974459060","https://openalex.org/W1994833963","https://openalex.org/W2021857616","https://openalex.org/W2031172850","https://openalex.org/W2036213650","https://openalex.org/W2043653481","https://openalex.org/W2045396361","https://openalex.org/W2061144551","https://openalex.org/W2069626069","https://openalex.org/W2072475940","https://openalex.org/W2092127669","https://openalex.org/W2096285034","https://openalex.org/W2104624927","https://openalex.org/W2105963649","https://openalex.org/W2125107816","https://openalex.org/W2133118715","https://openalex.org/W2192203593","https://openalex.org/W2290394775","https://openalex.org/W2552395941","https://openalex.org/W2736380262","https://openalex.org/W2791611621","https://openalex.org/W2793206677","https://openalex.org/W2800310459","https://openalex.org/W2916406145","https://openalex.org/W2963242019","https://openalex.org/W3000877573","https://openalex.org/W3048577892","https://openalex.org/W3049367628","https://openalex.org/W4211177544"],"related_works":["https://openalex.org/W3122388856","https://openalex.org/W2509820422","https://openalex.org/W2783425804","https://openalex.org/W2114625140","https://openalex.org/W2176075850","https://openalex.org/W3019472554","https://openalex.org/W2115621436","https://openalex.org/W1530226021","https://openalex.org/W4320931070","https://openalex.org/W2949522784"],"abstract_inverted_index":{"Given":[0],"a":[1,18,60,69,107,111,129,147],"set":[2],"of":[3,6,20,40,51,80,124,149,164,199],"competing":[4,86,126],"models":[5],"some":[7],"phenomenon":[8],"together":[9],"with":[10],"measurement":[11,53],"data,":[12],"Bayesian":[13,41,74],"model":[14,23,42,75,87,118,193],"selection":[15],"(BMS)":[16],"is":[17,25,45,168],"process":[19],"finding":[21],"the":[22,26,31,38,48,52,56,98,125,144,162,174,179,188,191,196,200],"that":[24],"best":[27],"candidate":[28],"for":[29,72,96],"being":[30],"true":[32],"data-generating":[33],"process.":[34],"BMS":[35],"relies":[36],"on":[37,122,190],"computation":[39],"evidence,":[43],"which":[44],"defined":[46],"as":[47,185],"marginal":[49],"likelihood":[50,58],"data":[54],"(i.e.,":[55],"average":[57],"over":[59],"model's":[61,138],"parameter":[62,139,201],"space).":[63],"In":[64,141],"this":[65],"article,":[66],"we":[67],"introduce":[68],"new":[70],"method":[71,78,145],"computing":[73],"evidence.":[76],"Our":[77],"consists":[79],"three":[81],"key":[82],"elements.":[83],"First,":[84],"all":[85],"functions":[88],"are":[89,101,120],"emulated":[90],"by":[91,104],"Gaussian":[92,99,165],"processes.":[93],"Model":[94],"evaluations":[95,119],"training":[97],"processes":[100,166],"chosen":[102],"one":[103,105],"in":[106,136,159,171,195],"sequential":[108,130],"manner.":[109],"Second,":[110],"model-time":[112,175],"allocation":[113,176],"strategy":[114,132,177,181],"decides":[115],"how":[116],"many":[117],"spent":[121],"each":[123,137],"models.":[127],"Third,":[128],"sampling":[131,180],"selects":[133],"design":[134],"points":[135],"space.":[140],"numerical":[142],"experiments,":[143],"shows":[146],"speed-up":[148],"more":[150,183],"than":[151],"1,000":[152],"compared":[153],"to":[154],"Monte":[155],"Carlo":[156],"estimation.":[157],"While,":[158],"lower-dimensional":[160],"cases,":[161,173],"use":[163],"alone":[167],"very":[169],"effective,":[170],"higher-dimensional":[172],"and":[178,194],"become":[182],"important":[184],"they":[186],"focus":[187],"effort":[189],"right":[192,197],"areas":[198],"domains.":[202]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}