{"id":"https://openalex.org/W2337228579","doi":"https://doi.org/10.1137/15m1043303","title":"Efficient Bayesian Experimentation Using an Expected Information Gain Lower Bound","display_name":"Efficient Bayesian Experimentation Using an Expected Information Gain Lower Bound","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2337228579","doi":"https://doi.org/10.1137/15m1043303","mag":"2337228579"},"language":"en","primary_location":{"id":"doi:10.1137/15m1043303","is_oa":false,"landing_page_url":"https://doi.org/10.1137/15m1043303","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":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1506.00053","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015839438","display_name":"Panagiotis Tsilifis","orcid":"https://orcid.org/0000-0001-5830-8508"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Panagiotis Tsilifis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006138292","display_name":"Roger Ghanem","orcid":"https://orcid.org/0000-0002-1890-920X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roger G. Ghanem","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5061531697","display_name":"Paris Hajali","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paris Hajali","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5015839438"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2261,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60161313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"5","issue":"1","first_page":"30","last_page":"62"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9983000159263611,"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"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9851999878883362,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9818000197410583,"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/computer-science","display_name":"Computer science","score":0.6770602464675903},{"id":"https://openalex.org/keywords/bayesian-experimental-design","display_name":"Bayesian experimental design","score":0.6751729846000671},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6676764488220215},{"id":"https://openalex.org/keywords/information-gain","display_name":"Information gain","score":0.6344577074050903},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5666230916976929},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5517433285713196},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5265522003173828},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4914332628250122},{"id":"https://openalex.org/keywords/polynomial-chaos","display_name":"Polynomial chaos","score":0.4278976619243622},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42658936977386475},{"id":"https://openalex.org/keywords/optimal-design","display_name":"Optimal design","score":0.4172179102897644},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2570916414260864},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20698857307434082},{"id":"https://openalex.org/keywords/bayesian-statistics","display_name":"Bayesian statistics","score":0.20262092351913452},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18951860070228577},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16340383887290955},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11815452575683594}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6770602464675903},{"id":"https://openalex.org/C99173435","wikidata":"https://www.wikidata.org/wiki/Q4874469","display_name":"Bayesian experimental design","level":5,"score":0.6751729846000671},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6676764488220215},{"id":"https://openalex.org/C2983203078","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Information gain","level":2,"score":0.6344577074050903},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5666230916976929},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5517433285713196},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5265522003173828},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4914332628250122},{"id":"https://openalex.org/C197656079","wikidata":"https://www.wikidata.org/wiki/Q17147719","display_name":"Polynomial chaos","level":3,"score":0.4278976619243622},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42658936977386475},{"id":"https://openalex.org/C186394612","wikidata":"https://www.wikidata.org/wiki/Q7098942","display_name":"Optimal design","level":2,"score":0.4172179102897644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2570916414260864},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20698857307434082},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.20262092351913452},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18951860070228577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16340383887290955},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11815452575683594}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1137/15m1043303","is_oa":false,"landing_page_url":"https://doi.org/10.1137/15m1043303","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"},{"id":"pmh:oai:arXiv.org:1506.00053","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1506.00053","pdf_url":"https://arxiv.org/pdf/1506.00053","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"},{"id":"mag:2337228579","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1506.00053","pdf_url":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1506.00053","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1506.00053","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1506.00053","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1506.00053","pdf_url":"https://arxiv.org/pdf/1506.00053","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":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2337228579.pdf","grobid_xml":"https://content.openalex.org/works/W2337228579.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1574558736","https://openalex.org/W1963846586","https://openalex.org/W1965555277","https://openalex.org/W1969131157","https://openalex.org/W1977168166","https://openalex.org/W1983671004","https://openalex.org/W1988288472","https://openalex.org/W1991079954","https://openalex.org/W1994616650","https://openalex.org/W1995780830","https://openalex.org/W2009797711","https://openalex.org/W2010737928","https://openalex.org/W2014018052","https://openalex.org/W2018011256","https://openalex.org/W2018159038","https://openalex.org/W2019143734","https://openalex.org/W2023575086","https://openalex.org/W2026679679","https://openalex.org/W2035814907","https://openalex.org/W2056760934","https://openalex.org/W2057803893","https://openalex.org/W2060496070","https://openalex.org/W2066802940","https://openalex.org/W2076580309","https://openalex.org/W2101480682","https://openalex.org/W2124289529","https://openalex.org/W2128452997","https://openalex.org/W2136602340","https://openalex.org/W2138309709","https://openalex.org/W2149498546","https://openalex.org/W2158323410","https://openalex.org/W2160518205","https://openalex.org/W2464471867"],"related_works":["https://openalex.org/W3179686269","https://openalex.org/W2923247998","https://openalex.org/W1517116813","https://openalex.org/W3006585854","https://openalex.org/W2873705236","https://openalex.org/W3196369922","https://openalex.org/W3002280017","https://openalex.org/W3133918909","https://openalex.org/W2982165393","https://openalex.org/W3038622104","https://openalex.org/W3212160501","https://openalex.org/W3033475332","https://openalex.org/W3182064858","https://openalex.org/W2954516367","https://openalex.org/W2803936899","https://openalex.org/W2916244421","https://openalex.org/W1800843408","https://openalex.org/W2997102038","https://openalex.org/W2317363656","https://openalex.org/W3032258136"],"abstract_inverted_index":{"Experimental":[0],"design":[1,67,110],"is":[2,43,105,136,181],"crucial":[3],"for":[4,87],"inference":[5,130],"where":[6,96,145,186],"limitations":[7],"in":[8,27,131,183],"the":[9,32,34,48,58,61,101,116,140,148,153,168,174],"data":[10,33],"collection":[11],"procedure":[12],"are":[13,189],"present":[14],"due":[15],"to":[16,45,47,57,114,138,158],"cost":[17],"or":[18],"other":[19],"restrictions.":[20],"Optimal":[21],"experimental":[22],"designs":[23],"determine":[24],"parameters":[25],"that":[26,171],"some":[28],"appropriate":[29],"sense":[30],"make":[31],"most":[35],"informative":[36],"possible.":[37],"In":[38,112],"a":[39,66,94,97,132,163,184],"Bayesian":[40],"setting":[41,185],"this":[42,119],"translated":[44],"updating":[46],"best":[49],"possible":[50],"posterior.":[51],"Information":[52],"theoretic":[53],"arguments":[54],"have":[55],"led":[56],"formation":[59],"of":[60,100,128,142,152,167],"expected":[62,102],"information":[63,103],"gain":[64,104],"as":[65,107],"criterion.":[68,111],"This":[69],"can":[70],"be":[71],"evaluated":[72],"mainly":[73],"by":[74,80],"Monte":[75],"Carlo":[76],"sampling":[77],"and":[78,162],"maximized":[79],"using":[81],"stochastic":[82],"approximation":[83,166],"methods,":[84],"both":[85],"known":[86],"being":[88],"computationally":[89],"expensive":[90],"tasks.":[91],"We":[92],"propose":[93],"framework":[95],"lower":[98],"bound":[99],"used":[106,137],"an":[108],"alternative":[109],"addition":[113],"alleviating":[115],"computational":[117],"burden,":[118],"also":[120],"addresses":[121],"issues":[122],"concerning":[123],"estimation":[124],"bias.":[125],"The":[126,178],"problem":[127],"permeability":[129],"large":[133],"contaminated":[134],"area":[135],"demonstrate":[139],"validity":[141],"our":[143],"approach":[144],"we":[146],"employ":[147],"massively":[149],"parallel":[150],"version":[151],"multiphase":[154],"multicomponent":[155],"simulator":[156],"TOUGH2":[157],"simulate":[159],"contaminant":[160],"transport":[161],"polynomial":[164],"chaos":[165],"forward":[169],"model":[170],"further":[172],"accelerates":[173],"objective":[175],"function":[176],"evaluations.":[177],"proposed":[179],"methodology":[180],"demonstrated":[182],"field":[187],"measurements":[188],"available.":[190]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
