{"id":"https://openalex.org/W2946428666","doi":"https://doi.org/10.1109/access.2020.3009878","title":"Approximate Bayesian Computation Via the Energy Statistic","display_name":"Approximate Bayesian Computation Via the Energy Statistic","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W2946428666","doi":"https://doi.org/10.1109/access.2020.3009878","mag":"2946428666"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3009878","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3009878","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09142178.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09142178.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037340964","display_name":"Hien D. Nguyen","orcid":"https://orcid.org/0000-0002-9958-432X"},"institutions":[{"id":"https://openalex.org/I196829312","display_name":"La Trobe University","ror":"https://ror.org/01rxfrp27","country_code":"AU","type":"education","lineage":["https://openalex.org/I196829312"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hien Duy Nguyen","raw_affiliation_strings":["Department of Mathematics and Statistics, La Trobe University, Melbourne, Australia",", La Trobe University"],"raw_orcid":"https://orcid.org/0000-0002-9958-432X","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, La Trobe University, Melbourne, Australia","institution_ids":["https://openalex.org/I196829312"]},{"raw_affiliation_string":", La Trobe University","institution_ids":["https://openalex.org/I196829312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054605178","display_name":"Julyan Arbel","orcid":"https://orcid.org/0000-0002-2525-4416"},"institutions":[{"id":"https://openalex.org/I106785703","display_name":"Institut polytechnique de Grenoble","ror":"https://ror.org/05sbt2524","country_code":"FR","type":"education","lineage":["https://openalex.org/I106785703","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210101348","display_name":"Centre Inria de l'Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/00n8d6z93","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1326498283","https://openalex.org/I4210101348"]},{"id":"https://openalex.org/I4210149092","display_name":"Laboratoire Jean Kuntzmann","ror":"https://ror.org/04ett5b41","country_code":"FR","type":"facility","lineage":["https://openalex.org/I106785703","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I4210149092","https://openalex.org/I899635006","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I899635006","display_name":"Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/02rx3b187","country_code":"FR","type":"education","lineage":["https://openalex.org/I899635006"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Julyan Arbel","raw_affiliation_strings":["Inria, CNRS, Grenoble INP, LJK, University Grenoble Alpes, Grenoble, France","Modelling and Inference of Complex and Structured Stochastic Systems"],"raw_orcid":"https://orcid.org/0000-0002-2525-4416","affiliations":[{"raw_affiliation_string":"Inria, CNRS, Grenoble INP, LJK, University Grenoble Alpes, Grenoble, France","institution_ids":["https://openalex.org/I106785703","https://openalex.org/I4210149092","https://openalex.org/I4210101348","https://openalex.org/I899635006","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Modelling and Inference of Complex and Structured Stochastic Systems","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060780160","display_name":"Hongliang L\u00fc","orcid":"https://orcid.org/0000-0002-0073-8991"},"institutions":[{"id":"https://openalex.org/I106785703","display_name":"Institut polytechnique de Grenoble","ror":"https://ror.org/05sbt2524","country_code":"FR","type":"education","lineage":["https://openalex.org/I106785703","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210101348","display_name":"Centre Inria de l'Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/00n8d6z93","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1326498283","https://openalex.org/I4210101348"]},{"id":"https://openalex.org/I4210149092","display_name":"Laboratoire Jean Kuntzmann","ror":"https://ror.org/04ett5b41","country_code":"FR","type":"facility","lineage":["https://openalex.org/I106785703","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I4210149092","https://openalex.org/I899635006","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I899635006","display_name":"Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/02rx3b187","country_code":"FR","type":"education","lineage":["https://openalex.org/I899635006"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hongliang Lu","raw_affiliation_strings":["Inria, CNRS, Grenoble INP, LJK, University Grenoble Alpes, Grenoble, France","Modelling and Inference of Complex and Structured Stochastic Systems"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inria, CNRS, Grenoble INP, LJK, University Grenoble Alpes, Grenoble, France","institution_ids":["https://openalex.org/I106785703","https://openalex.org/I4210149092","https://openalex.org/I4210101348","https://openalex.org/I899635006","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Modelling and Inference of Complex and Structured Stochastic Systems","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088691547","display_name":"Florence Forbes","orcid":"https://orcid.org/0000-0003-3639-0226"},"institutions":[{"id":"https://openalex.org/I106785703","display_name":"Institut polytechnique de Grenoble","ror":"https://ror.org/05sbt2524","country_code":"FR","type":"education","lineage":["https://openalex.org/I106785703","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210101348","display_name":"Centre Inria de l'Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/00n8d6z93","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1326498283","https://openalex.org/I4210101348"]},{"id":"https://openalex.org/I4210149092","display_name":"Laboratoire Jean Kuntzmann","ror":"https://ror.org/04ett5b41","country_code":"FR","type":"facility","lineage":["https://openalex.org/I106785703","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I4210149092","https://openalex.org/I899635006","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I899635006","display_name":"Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/02rx3b187","country_code":"FR","type":"education","lineage":["https://openalex.org/I899635006"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Florence Forbes","raw_affiliation_strings":["Inria, CNRS, Grenoble INP, LJK, University Grenoble Alpes, Grenoble, France","Modelling and Inference of Complex and Structured Stochastic Systems"],"raw_orcid":"https://orcid.org/0000-0003-3639-0226","affiliations":[{"raw_affiliation_string":"Inria, CNRS, Grenoble INP, LJK, University Grenoble Alpes, Grenoble, France","institution_ids":["https://openalex.org/I106785703","https://openalex.org/I4210149092","https://openalex.org/I4210101348","https://openalex.org/I899635006","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Modelling and Inference of Complex and Structured Stochastic Systems","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.0652,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.77066538,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"8","issue":null,"first_page":"131683","last_page":"131698"},"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":0.991100013256073,"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":0.991100013256073,"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":0.0019000000320374966,"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/T12404","display_name":"Mathematical Approximation and Integration","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/2612","display_name":"Numerical Analysis"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.7193813323974609},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6765543818473816},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6586164832115173},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5014297962188721},{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.47542479634284973},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.41321074962615967},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.37790918350219727},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3699755072593689},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33872342109680176},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32863694429397583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.237084299325943}],"concepts":[{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.7193813323974609},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6765543818473816},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6586164832115173},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5014297962188721},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.47542479634284973},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.41321074962615967},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37790918350219727},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3699755072593689},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33872342109680176},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32863694429397583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.237084299325943},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/access.2020.3009878","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3009878","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09142178.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1905.05884","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.05884","pdf_url":"https://arxiv.org/pdf/1905.05884","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":null},{"id":"pmh:oai:HAL:hal-02399934v1","is_oa":false,"landing_page_url":"https://hal.science/hal-02399934","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, 2020, 8, pp.131683-131698. &#x27E8;10.1109/access.2020.3009878&#x27E9;","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:371f7d125ba54e3da48cbda621d1e730","is_oa":true,"landing_page_url":"https://doaj.org/article/371f7d125ba54e3da48cbda621d1e730","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 131683-131698 (2020)","raw_type":"article"},{"id":"doi:10.48550/arxiv.1905.05884","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1905.05884","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-journal"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3009878","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3009878","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09142178.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8600000143051147}],"awards":[{"id":"https://openalex.org/G3902238193","display_name":"Classification methods for providing personalised and class decisions","funder_award_id":"DP180101192","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G66823034","display_name":"Feasible algorithms for big inference.","funder_award_id":"DE170101134","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320320577","display_name":"La Trobe University","ror":"https://ror.org/01rxfrp27"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2946428666.pdf","grobid_xml":"https://content.openalex.org/works/W2946428666.grobid-xml"},"referenced_works_count":63,"referenced_works":["https://openalex.org/W618612974","https://openalex.org/W656236630","https://openalex.org/W1421469996","https://openalex.org/W1502641836","https://openalex.org/W1503398984","https://openalex.org/W1590183771","https://openalex.org/W1594863551","https://openalex.org/W1617959293","https://openalex.org/W1686367817","https://openalex.org/W1779010541","https://openalex.org/W1793259860","https://openalex.org/W1810615071","https://openalex.org/W1976906336","https://openalex.org/W2011292888","https://openalex.org/W2011626820","https://openalex.org/W2012933304","https://openalex.org/W2016546007","https://openalex.org/W2031134376","https://openalex.org/W2034795216","https://openalex.org/W2040011853","https://openalex.org/W2065134558","https://openalex.org/W2065392673","https://openalex.org/W2083252561","https://openalex.org/W2086421081","https://openalex.org/W2109638305","https://openalex.org/W2116416291","https://openalex.org/W2139812092","https://openalex.org/W2149531814","https://openalex.org/W2182515964","https://openalex.org/W2212660284","https://openalex.org/W2257327159","https://openalex.org/W2263255029","https://openalex.org/W2487173427","https://openalex.org/W2494636976","https://openalex.org/W2534708821","https://openalex.org/W2764892793","https://openalex.org/W2797333853","https://openalex.org/W2798792107","https://openalex.org/W2895152177","https://openalex.org/W2898126478","https://openalex.org/W2907302549","https://openalex.org/W2916041869","https://openalex.org/W2950536412","https://openalex.org/W2962767364","https://openalex.org/W2962771927","https://openalex.org/W2962900370","https://openalex.org/W2962903918","https://openalex.org/W2963134136","https://openalex.org/W2963297889","https://openalex.org/W2963495973","https://openalex.org/W2963977539","https://openalex.org/W2973214398","https://openalex.org/W2999898255","https://openalex.org/W3122091473","https://openalex.org/W3123857276","https://openalex.org/W4206341000","https://openalex.org/W4248183269","https://openalex.org/W6676531042","https://openalex.org/W6678814708","https://openalex.org/W6688325169","https://openalex.org/W6693409169","https://openalex.org/W6728691686","https://openalex.org/W6750836980"],"related_works":["https://openalex.org/W1985846739","https://openalex.org/W2146620998","https://openalex.org/W2359776416","https://openalex.org/W2254500280","https://openalex.org/W3123699969","https://openalex.org/W2353788488","https://openalex.org/W4297813618","https://openalex.org/W2733224243","https://openalex.org/W2226294016","https://openalex.org/W4221142065"],"abstract_inverted_index":{"Approximate":[0],"Bayesian":[1,11],"computation":[2],"(ABC)":[3],"has":[4,61],"become":[5],"an":[6],"essential":[7],"part":[8],"of":[9,48,70,134,157,219,228],"the":[10,18,46,68,78,85,97,101,106,119,124,131,158,166,174,180,190,203],"toolbox":[12],"for":[13,96,146],"addressing":[14],"problems":[15],"in":[16,64,130,202],"which":[17,49,114,162],"likelihood":[19],"is":[20,50,214],"prohibitively":[21],"expensive":[22],"or":[23],"entirely":[24],"unknown,":[25],"making":[26],"it":[27],"intractable.":[28],"ABC":[29,80,148,176,212],"defines":[30],"a":[31,53,92,153,217,222,225,231,235],"pseudo-posterior":[32,184],"by":[33],"comparing":[34],"observed":[35,102],"data":[36,58,108,120],"with":[37,195,247],"simulated":[38,107],"data,":[39],"traditionally":[40],"based":[41,178,211],"on":[42,84,179,216],"some":[43],"summary":[44,71],"statistics,":[45],"elicitation":[47],"regarded":[51],"as":[52],"key":[54],"difficulty.":[55],"Recently,":[56],"using":[57],"discrepancy":[59,121,249],"measures":[60],"been":[62,143],"proposed":[63,208,243],"order":[65,229],"to":[66,76,112,116,189,200],"bypass":[67],"construction":[69],"statistics.":[72],"Here":[73],"we":[74,151,163],"propose":[75,152],"use":[77],"importance-sampling":[79],"(IS-ABC)":[81],"algorithm":[82,213],"relying":[83],"so-called":[86],"two-sample":[87],"energy":[88,159,181,209],"statistic.":[89],"We":[90,239],"establish":[91],"new":[93],"asymptotic":[94,125],"result":[95,128,169],"case":[98],"where":[99],"both":[100],"sample":[103,109,168,205],"size":[104,110],"and":[105,171,234],"increase":[111],"infinity,":[113],"highlights":[115],"what":[117],"extent":[118],"measure":[122],"impacts":[123],"pseudo-posterior.":[126],"The":[127],"holds":[129],"broad":[132],"setting":[133],"IS-ABC":[135],"methodologies,":[136],"thus":[137],"generalizing":[138],"previous":[139],"results":[140],"that":[141,165,173,186,198,241],"have":[142],"established":[144],"only":[145],"rejection":[147,175,196],"algorithms.":[149],"Furthermore,":[150],"consistent":[154],"V-statistic":[155],"estimator":[156],"statistic,":[160,182],"under":[161],"show":[164],"large":[167],"holds,":[170],"prove":[172],"algorithm,":[177],"generates":[183],"distributions":[185],"achieves":[187],"convergence":[188],"correct":[191],"limits,":[192],"when":[193],"implemented":[194],"thresholds":[197],"converge":[199],"zero,":[201],"finite":[204],"setting.":[206],"Our":[207],"statistic":[210],"demonstrated":[215],"variety":[218],"models,":[220],"including":[221],"Gaussian":[223],"mixture,":[224],"moving-average":[226],"model":[227],"two,":[230],"bivariate":[232],"beta":[233],"multivariate":[236],"g-and-k":[237],"distribution.":[238],"find":[240],"our":[242],"method":[244],"compares":[245],"well":[246],"alternative":[248],"measures.":[250]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":4}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2019-05-29T00:00:00"}
