{"id":"https://openalex.org/W3033895182","doi":"https://doi.org/10.1080/10618600.2020.1775617","title":"Delayed Acceptance ABC-SMC","display_name":"Delayed Acceptance ABC-SMC","publication_year":2020,"publication_date":"2020-06-02","ids":{"openalex":"https://openalex.org/W3033895182","doi":"https://doi.org/10.1080/10618600.2020.1775617","mag":"3033895182"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2020.1775617","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2020.1775617","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1080/10618600.2020.1775617","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008967164","display_name":"Richard G. Everitt","orcid":"https://orcid.org/0000-0002-0822-5648"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Richard G. Everitt","raw_affiliation_strings":["Department of Statistics, University of Warwick, Coventry, UK"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Warwick, Coventry, UK","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089891041","display_name":"Paulina A. Rowi\u0144ska","orcid":null},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Paulina A. Rowi\u0144ska","raw_affiliation_strings":["Department of Mathematics, Imperial College London, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Imperial College London, London, UK","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008967164"],"corresponding_institution_ids":["https://openalex.org/I39555362"],"apc_list":null,"apc_paid":null,"fwci":1.9437,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.87556815,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"30","issue":"1","first_page":"55","last_page":"66"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9966999888420105,"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":0.9965999722480774,"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/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.8210386633872986},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.65952467918396},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5894923210144043},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5602723956108093},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5371039509773254},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4859665632247925},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.4338299036026001},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.42915672063827515},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4279515743255615},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.410504013299942},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3880677819252014},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2828018069267273},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.23019462823867798},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22471749782562256},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2190801203250885}],"concepts":[{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.8210386633872986},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.65952467918396},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5894923210144043},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5602723956108093},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5371039509773254},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4859665632247925},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.4338299036026001},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.42915672063827515},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4279515743255615},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.410504013299942},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3880677819252014},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2828018069267273},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.23019462823867798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22471749782562256},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2190801203250885},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/10618600.2020.1775617","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2020.1775617","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},{"id":"pmh:oai:wrap.warwick.ac.uk:137316","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400665","display_name":"Warwick Research Archive Portal (University of Warwick)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39555362","host_organization_name":"University of Warwick","host_organization_lineage":["https://openalex.org/I39555362"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.1080/10618600.2020.1775617","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2020.1775617","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1726514504","display_name":"Understanding recombination through tractable statistical analysis of whole genome sequences","funder_award_id":"BB/N00874X/1","funder_id":"https://openalex.org/F4320334629","funder_display_name":"Biotechnology and Biological Sciences Research Council"},{"id":"https://openalex.org/G2351504152","display_name":null,"funder_award_id":"EP/N023927/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G3564511431","display_name":"Tractable inference for statistical network models with local dependence","funder_award_id":"EP/N023927/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G865151178","display_name":null,"funder_award_id":"BB/N00874X/1","funder_id":"https://openalex.org/F4320334629","funder_display_name":"Biotechnology and Biological Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"},{"id":"https://openalex.org/F4320334629","display_name":"Biotechnology and Biological Sciences Research Council","ror":"https://ror.org/00cwqg982"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W596688237","https://openalex.org/W1533758202","https://openalex.org/W1566525371","https://openalex.org/W1891298814","https://openalex.org/W1967288931","https://openalex.org/W1978990433","https://openalex.org/W1983431486","https://openalex.org/W2000186700","https://openalex.org/W2034795216","https://openalex.org/W2042770989","https://openalex.org/W2043983054","https://openalex.org/W2057487175","https://openalex.org/W2063066676","https://openalex.org/W2067392831","https://openalex.org/W2091860746","https://openalex.org/W2114456110","https://openalex.org/W2114964853","https://openalex.org/W2116416291","https://openalex.org/W2125280835","https://openalex.org/W2127281696","https://openalex.org/W2135267747","https://openalex.org/W2147357149","https://openalex.org/W2151729750","https://openalex.org/W2155418451","https://openalex.org/W2166215379","https://openalex.org/W2195963939","https://openalex.org/W2225674876","https://openalex.org/W2398350456","https://openalex.org/W2553418811","https://openalex.org/W2567289819","https://openalex.org/W2581258669","https://openalex.org/W2582743722","https://openalex.org/W2613615430","https://openalex.org/W2763783893","https://openalex.org/W2911248615","https://openalex.org/W2950543536","https://openalex.org/W2962767364","https://openalex.org/W3098926670","https://openalex.org/W3100622648","https://openalex.org/W3101747691","https://openalex.org/W3121623881","https://openalex.org/W3125235483","https://openalex.org/W4205814206","https://openalex.org/W4399611993","https://openalex.org/W6631938799"],"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/W2032616735","https://openalex.org/W2733224243","https://openalex.org/W4299931562","https://openalex.org/W3010690761","https://openalex.org/W68766333"],"abstract_inverted_index":{"Approximate":[0],"Bayesian":[1],"computation":[2],"(ABC)":[3],"is":[4,19,34,70,83,99,103,152,164],"now":[5],"an":[6,114],"established":[7],"technique":[8],"for":[9,80],"statistical":[10],"inference":[11],"used":[12,181],"in":[13,36,74,91,121,124],"cases":[14,75,92],"where":[15,76,93,162],"the":[16,28,37,44,59,64,78,128,131,139,149,156,160],"likelihood":[17,45],"function":[18],"computationally":[20,72],"expensive":[21],"or":[22],"not":[23,71,144],"available.":[24,100],"It":[25],"relies":[26],"on":[27],"use":[29,130],"of":[30,39,61,127,138,159,168,172],"a":[31,40,47,94,165],"model":[32],"that":[33,142,148,176],"specified":[35],"form":[38],"simulator,":[41],"and":[42,57,194],"approximates":[43],"at":[46],"parameter":[48,140],"value":[49],"$\\\\theta$":[50,82],"by":[51],"simulating":[52],"auxiliary":[53],"data":[54,66],"sets":[55],"$x$":[56,62],"evaluating":[58],"distance":[60],"from":[63],"true":[65],"$y$.":[67],"However,":[68],"ABC":[69,115],"feasible":[73],"using":[77],"simulator":[79,98,133,151],"each":[81],"very":[84],"expensive.":[85],"This":[86],"paper":[87],"investigates":[88],"this":[89,177],"situation":[90],"cheap,":[95],"but":[96],"approximate,":[97],"The":[101],"approach":[102,178],"to":[104,134,189],"employ":[105],"delayed":[106],"acceptance":[107],"Markov":[108],"chain":[109],"Monte":[110,117],"Carlo":[111,118],"(MCMC)":[112],"within":[113],"sequential":[116],"(SMC)":[119],"sampler":[120],"order":[122],"to,":[123],"a~first":[125],"stage":[126,158],"kernel,":[129],"cheap":[132],"rule":[135],"out":[136],"parts":[137],"space":[141],"are":[143,199],"worth":[145],"exploring,":[146],"so":[147],"``true''":[150],"only":[153],"run":[154],"(in":[155],"second":[157],"kernel)":[161],"there":[163],"reasonable":[166],"chance":[167],"accepting":[169],"proposed":[170],"values":[171],"$\\\\theta$.":[173],"We":[174],"show":[175],"can":[179],"be":[180],"quite":[182],"automatically,":[183],"with":[184],"few":[185],"tuning":[186],"parameters.":[187],"Applications":[188],"stochastic":[190],"differential":[191],"equation":[192],"models":[193],"latent":[195],"doubly":[196],"intractable":[197],"distributions":[198],"presented.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}