{"id":"https://openalex.org/W4298144027","doi":"https://doi.org/10.1007/s11222-022-10159-2","title":"Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models","display_name":"Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models","publication_year":2022,"publication_date":"2022-09-30","ids":{"openalex":"https://openalex.org/W4298144027","doi":"https://doi.org/10.1007/s11222-022-10159-2"},"language":"en","primary_location":{"id":"doi:10.1007/s11222-022-10159-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-022-10159-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-022-10159-2.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11222-022-10159-2.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044963078","display_name":"Yiolanda Englezou","orcid":"https://orcid.org/0000-0003-2826-0501"},"institutions":[{"id":"https://openalex.org/I34771391","display_name":"University of Cyprus","ror":"https://ror.org/02qjrjx09","country_code":"CY","type":"education","lineage":["https://openalex.org/I34771391"]}],"countries":["CY"],"is_corresponding":false,"raw_author_name":"Yiolanda Englezou","raw_affiliation_strings":["KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus"],"raw_orcid":"https://orcid.org/0000-0003-2826-0501","affiliations":[{"raw_affiliation_string":"KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus","institution_ids":["https://openalex.org/I34771391"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060738844","display_name":"Timothy W. Waite","orcid":"https://orcid.org/0000-0002-8868-3146"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Timothy W. Waite","raw_affiliation_strings":["Department of Mathematics, University of Manchester, Manchester, UK"],"raw_orcid":"https://orcid.org/0000-0002-8868-3146","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Manchester, Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001047279","display_name":"David C. Woods","orcid":"https://orcid.org/0000-0001-7648-429X"},"institutions":[{"id":"https://openalex.org/I43439940","display_name":"University of Southampton","ror":"https://ror.org/01ryk1543","country_code":"GB","type":"education","lineage":["https://openalex.org/I43439940"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"David C. Woods","raw_affiliation_strings":["Statistical Sciences Research Institute, University of Southampton, Southampton, UK"],"raw_orcid":"https://orcid.org/0000-0001-7648-429X","affiliations":[{"raw_affiliation_string":"Statistical Sciences Research Institute, University of Southampton, Southampton, UK","institution_ids":["https://openalex.org/I43439940"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060738844"],"corresponding_institution_ids":["https://openalex.org/I28407311"],"apc_list":{"value":2690,"currency":"USD","value_usd":2690},"apc_paid":{"value":2690,"currency":"USD","value_usd":2690},"fwci":1.0524,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.78262872,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"32","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9998999834060669,"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":0.9998999834060669,"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.9998000264167786,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9983000159263611,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/laplaces-method","display_name":"Laplace's method","score":0.7410057783126831},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5935544967651367},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5460127592086792},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5333514213562012},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4881006181240082},{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.483095645904541},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.47995856404304504},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.46719738841056824},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4597250819206238},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4537525177001953},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43621790409088135},{"id":"https://openalex.org/keywords/laplace-transform","display_name":"Laplace transform","score":0.42913100123405457},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4269024729728699},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4103865623474121},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.36672550439834595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21634700894355774},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21353697776794434}],"concepts":[{"id":"https://openalex.org/C22243797","wikidata":"https://www.wikidata.org/wiki/Q2058297","display_name":"Laplace's method","level":3,"score":0.7410057783126831},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5935544967651367},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5460127592086792},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5333514213562012},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4881006181240082},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.483095645904541},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.47995856404304504},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.46719738841056824},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4597250819206238},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4537525177001953},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43621790409088135},{"id":"https://openalex.org/C97937538","wikidata":"https://www.wikidata.org/wiki/Q199691","display_name":"Laplace transform","level":2,"score":0.42913100123405457},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4269024729728699},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4103865623474121},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.36672550439834595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21634700894355774},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21353697776794434},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s11222-022-10159-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-022-10159-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-022-10159-2.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:openaire/4308533f-b1c1-4572-8281-21cfc81e6911","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/4308533f-b1c1-4572-8281-21cfc81e6911","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Englezou, Y, Waite, T & Woods, D C 2022, 'Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models', Statistics and Computing, vol. 32, 82. https://doi.org/10.1007/s11222-022-10159-2","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:eprints.soton.ac.uk:470495","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401019","display_name":"ePrints Soton (University of Southampton)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I43439940","host_organization_name":"University of Southampton","host_organization_lineage":["https://openalex.org/I43439940"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"doi:10.1007/s11222-022-10159-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-022-10159-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-022-10159-2.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2672862473","display_name":null,"funder_award_id":"EP/J018317/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7329224357","display_name":"Statistical Design of Experiments for Complex Nonparametric and Mechanistic Models","funder_award_id":"EP/J018317/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4298144027.pdf","grobid_xml":"https://content.openalex.org/works/W4298144027.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W55534668","https://openalex.org/W87152119","https://openalex.org/W1969131157","https://openalex.org/W1983671004","https://openalex.org/W1995378794","https://openalex.org/W2014018052","https://openalex.org/W2019143734","https://openalex.org/W2027255954","https://openalex.org/W2031234795","https://openalex.org/W2060442802","https://openalex.org/W2070779353","https://openalex.org/W2076580309","https://openalex.org/W2082382318","https://openalex.org/W2106201557","https://openalex.org/W2120575449","https://openalex.org/W2159325249","https://openalex.org/W2586750255","https://openalex.org/W2764128407","https://openalex.org/W2970446088","https://openalex.org/W4248681815","https://openalex.org/W6600826560","https://openalex.org/W6607281299"],"related_works":["https://openalex.org/W2138213559","https://openalex.org/W2753218748","https://openalex.org/W2774409638","https://openalex.org/W2063830806","https://openalex.org/W1988195381","https://openalex.org/W3205185019","https://openalex.org/W2188349433","https://openalex.org/W2574967310","https://openalex.org/W2896567555","https://openalex.org/W2609965435"],"abstract_inverted_index":{"Abstract":[0],"One":[1],"of":[2,31,48,90,103,109,113,125,224,263],"the":[3,13,32,38,46,62,87,97,104,117,123,139,143,222,243,269],"major":[4],"challenges":[5],"in":[6,17,57,93,131,258],"Bayesian":[7],"optimal":[8],"design":[9,275],"is":[10,41,129,169,206,234],"to":[11,85,95,195,221,255],"approximate":[12,75,270],"expected":[14,53],"utility":[15,188],"function":[16],"an":[18,177,203],"accurate":[19,210],"and":[20,74,165,173,184,190,237,250],"computationally":[21],"efficient":[22,178,225],"manner.":[23],"We":[24,44],"focus":[25],"on":[26,69,142],"Shannon":[27,54],"information":[28,55],"gain,":[29],"one":[30,230],"most":[33],"widely":[34],"used":[35],"utilities":[36],"when":[37,266],"experimental":[39],"goal":[40],"parameter":[42,145],"inference.":[43],"compare":[45],"performance":[47],"various":[49],"methods":[50,150,257],"for":[51,107,116,187,242,274],"approximating":[52],"gain":[56],"common":[58],"nonlinear":[59],"models":[60],"from":[61],"statistics":[63],"literature,":[64],"with":[65,154,260,268],"a":[66,80,110,218],"particular":[67],"emphasis":[68],"Laplace":[70,76,166],"importance":[71,77,98,140,163],"sampling":[72,78],"(LIS)":[73],"(ALIS),":[79],"new":[81],"method":[82],"that":[83,128,171,205,248],"aims":[84],"reduce":[86],"computational":[88,185],"cost":[89,186],"LIS.":[91,199],"Specifically,":[92],"order":[94],"centre":[96],"distributions":[99,141],"LIS":[100,172,249],"requires":[101],"computation":[102],"posterior":[105],"mode":[106],"each":[108],"large":[111,261],"number":[112],"simulated":[114],"possibilities":[115],"response":[118],"vector.":[119],"ALIS":[120,174,191,201,233,251],"substantially":[121],"reduces":[122],"amount":[124],"numerical":[126],"optimization":[127],"required,":[130],"some":[132],"cases":[133],"eliminating":[134],"all":[135],"optimization,":[136],"by":[137],"centering":[138],"data-generating":[144],"values":[146],"wherever":[147],"possible.":[148],"Both":[149],"are":[151],"thoroughly":[152],"compared":[153],"existing":[155,256],"approximations":[156],"including":[157],"Double":[158],"Loop":[159],"Monte":[160],"Carlo,":[161],"nested":[162],"sampling,":[164],"approximation.":[167],"It":[168],"found":[170],"both":[175,235],"give":[176],"trade-off":[179],"between":[180],"mean":[181],"squared":[182],"error":[183],"estimation,":[189],"can":[192],"be":[193],"up":[194],"70%":[196],"cheaper":[197,207,236],"than":[198,211],"Usually":[200],"gives":[202],"approximation":[204],"but":[208],"less":[209],"LIS,":[212],"while":[213],"still":[214],"being":[215],"efficient,":[216],"giving":[217],"useful":[219],"addition":[220],"suite":[223],"methods.":[226],"However,":[227],"we":[228,246],"observed":[229],"case":[231],"where":[232],"more":[238],"accurate.":[239],"In":[240],"addition,":[241],"first":[244],"time":[245],"show":[247],"yield":[252],"superior":[253],"designs":[254],"problems":[259],"numbers":[262],"model":[264],"parameters":[265],"combined":[267],"co-ordinate":[271],"exchange":[272],"algorithm":[273],"optimization.":[276]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}