{"id":"https://openalex.org/W2903275657","doi":"https://doi.org/10.1137/18m123284x","title":"Information Geometry for Approximate Bayesian Computation","display_name":"Information Geometry for Approximate Bayesian Computation","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W2903275657","doi":"https://doi.org/10.1137/18m123284x","mag":"2903275657"},"language":"en","primary_location":{"id":"doi:10.1137/18m123284x","is_oa":false,"landing_page_url":"https://doi.org/10.1137/18m123284x","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/1812.02127","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103169334","display_name":"Konstantinos Spiliopoulos","orcid":"https://orcid.org/0000-0003-2021-6426"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Konstantinos Spiliopoulos","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5103169334"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00744879,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":"1","first_page":"229","last_page":"260"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9993000030517578,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9993000030517578,"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":0.9976999759674072,"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"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9969000220298767,"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/estimator","display_name":"Estimator","score":0.6635422706604004},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6411360502243042},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5146011114120483},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5022788047790527},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4986395835876465},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48784971237182617},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.482272207736969},{"id":"https://openalex.org/keywords/observable","display_name":"Observable","score":0.4719354808330536},{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.46419134736061096},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.46225211024284363},{"id":"https://openalex.org/keywords/kullback\u2013leibler-divergence","display_name":"Kullback\u2013Leibler divergence","score":0.4588805139064789},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.4489332437515259},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.431433767080307},{"id":"https://openalex.org/keywords/information-theory","display_name":"Information theory","score":0.42972615361213684},{"id":"https://openalex.org/keywords/transfer-entropy","display_name":"Transfer entropy","score":0.4117323160171509},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2746227979660034},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.26229214668273926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21846920251846313}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6635422706604004},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6411360502243042},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5146011114120483},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5022788047790527},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4986395835876465},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48784971237182617},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.482272207736969},{"id":"https://openalex.org/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.4719354808330536},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.46419134736061096},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.46225211024284363},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.4588805139064789},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.4489332437515259},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.431433767080307},{"id":"https://openalex.org/C52622258","wikidata":"https://www.wikidata.org/wiki/Q131222","display_name":"Information theory","level":2,"score":0.42972615361213684},{"id":"https://openalex.org/C182049051","wikidata":"https://www.wikidata.org/wiki/Q17147155","display_name":"Transfer entropy","level":3,"score":0.4117323160171509},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2746227979660034},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.26229214668273926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21846920251846313},{"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1137/18m123284x","is_oa":false,"landing_page_url":"https://doi.org/10.1137/18m123284x","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:1812.02127","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1812.02127","pdf_url":"https://arxiv.org/pdf/1812.02127","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":"","raw_type":"text"},{"id":"mag:2903275657","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1812.02127.pdf","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.1812.02127","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1812.02127","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:1812.02127","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1812.02127","pdf_url":"https://arxiv.org/pdf/1812.02127","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":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8710554737","display_name":null,"funder_award_id":"DMS-1550918","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G945892359","display_name":"CAREER: Multiscale stochastic processes, Monte Carlo Methods and Irreversibility","funder_award_id":"1550918","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2903275657.pdf","grobid_xml":"https://content.openalex.org/works/W2903275657.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W179554802","https://openalex.org/W1534416612","https://openalex.org/W1793259860","https://openalex.org/W1971722701","https://openalex.org/W1973099219","https://openalex.org/W2010470683","https://openalex.org/W2012933304","https://openalex.org/W2045973738","https://openalex.org/W2051618297","https://openalex.org/W2053332694","https://openalex.org/W2068483930","https://openalex.org/W2092124742","https://openalex.org/W2116416291","https://openalex.org/W2139812092","https://openalex.org/W2149455494","https://openalex.org/W2156984627","https://openalex.org/W2167933455","https://openalex.org/W2227162516","https://openalex.org/W2296319761","https://openalex.org/W2355724939","https://openalex.org/W2963703812","https://openalex.org/W2963977539","https://openalex.org/W3104712395"],"related_works":["https://openalex.org/W1896042095","https://openalex.org/W1953623653","https://openalex.org/W2965870078","https://openalex.org/W2993189505","https://openalex.org/W2948877301","https://openalex.org/W1981573976","https://openalex.org/W2750426698","https://openalex.org/W3111162488","https://openalex.org/W2781321050","https://openalex.org/W3115475828","https://openalex.org/W2162842498","https://openalex.org/W3134518563","https://openalex.org/W43459774","https://openalex.org/W3097813594","https://openalex.org/W2559375353","https://openalex.org/W2805030707","https://openalex.org/W1488322034","https://openalex.org/W2795530222","https://openalex.org/W2527419728","https://openalex.org/W2734601663"],"abstract_inverted_index":{"The":[0],"goal":[1],"of":[2,18,32,57,63,68,71,86,99,125,132,141,150,158],"this":[3],"paper":[4],"is":[5,22,35,77],"to":[6,37,53,95,114,121],"explore":[7,96],"the":[8,16,30,33,55,58,64,69,72,84,87,97,100,126,129,139,142,152,156,159,180],"basic":[9],"approximate":[10],"Bayesian":[11],"computation":[12],"(ABC)":[13],"algorithm":[14,26,59,127],"via":[15],"lens":[17],"information":[19],"theory.":[20],"ABC":[21],"a":[23,61,106,148],"widely":[24],"used":[25],"in":[27],"cases":[28],"where":[29],"likelihood":[31],"data":[34,78],"hard":[36],"work":[38],"with":[39],"or":[40],"intractable,":[41],"but":[42],"one":[43],"can":[44],"simulate":[45],"from":[46],"it.":[47],"We":[48],"use":[49],"relative":[50],"entropy":[51,75,91],"ideas":[52],"analyze":[54],"behavior":[56],"as":[60,80,147],"function":[62,149],"threshold":[65,153],"parameter":[66],"and":[67,103,155,171,178],"size":[70,157],"data.":[73,160],"Relative":[74,90],"here":[76],"driven,":[79],"it":[81],"depends":[82],"on":[83,166],"values":[85],"observed":[88],"statistics.":[89],"also":[92,137],"allows":[93],"us":[94,113],"effect":[98],"distance":[101],"metric":[102],"sets":[104],"up":[105],"mathematical":[107],"framework":[108],"for":[109,128,144,168],"sensitivity":[110],"analysis":[111,162],"allowing":[112],"find":[115],"important":[116],"directions":[117],"which":[118],"could":[119],"lead":[120],"lower":[122],"computational":[123],"cost":[124],"same":[130],"level":[131],"accuracy.":[133],"In":[134],"addition,":[135],"we":[136],"investigate":[138],"bias":[140],"estimators":[143],"generic":[145],"observables":[146],"both":[151],"parameters":[154],"Our":[161],"provides":[163],"error":[164],"bounds":[165],"performance":[167],"positive":[169],"tolerances":[170],"finite":[172],"sample":[173],"sizes.":[174],"Simulation":[175],"studies":[176],"complement":[177],"illustrate":[179],"theoretical":[181],"results.":[182]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}