{"id":"https://openalex.org/W4232843539","doi":"https://doi.org/10.1109/wsc.2018.8632239","title":"BETTER INPUT MODELING VIA MODEL AVERAGING","display_name":"BETTER INPUT MODELING VIA MODEL AVERAGING","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W4232843539","doi":"https://doi.org/10.1109/wsc.2018.8632239"},"language":"en","primary_location":{"id":"doi:10.1109/wsc.2018.8632239","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2018.8632239","pdf_url":null,"source":{"id":"https://openalex.org/S4363607834","display_name":"2018 Winter Simulation Conference (WSC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033701559","display_name":"Wendy Xi Jiang","orcid":"https://orcid.org/0000-0002-0083-5345"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wendy Xi Jiang","raw_affiliation_strings":["Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026731126","display_name":"Barry L. Nelson","orcid":"https://orcid.org/0000-0002-1325-2624"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Barry L. Nelson","raw_affiliation_strings":["Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5033701559"],"corresponding_institution_ids":["https://openalex.org/I111979921"],"apc_list":null,"apc_paid":null,"fwci":0.077,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.50741186,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1575","last_page":"1586"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9876999855041504,"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"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9876999855041504,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9710999727249146,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9697999954223633,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.7501118183135986},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.6739609837532043},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5431811213493347},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.5362846851348877},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5053925514221191},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.45660948753356934},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3942476511001587},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3796813488006592},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.35336172580718994},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33197021484375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30609625577926636},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3050130009651184},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.19642755389213562}],"concepts":[{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.7501118183135986},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.6739609837532043},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5431811213493347},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5362846851348877},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5053925514221191},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.45660948753356934},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3942476511001587},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3796813488006592},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35336172580718994},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33197021484375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30609625577926636},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3050130009651184},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.19642755389213562},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wsc.2018.8632239","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2018.8632239","pdf_url":null,"source":{"id":"https://openalex.org/S4363607834","display_name":"2018 Winter Simulation Conference (WSC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2077878098","https://openalex.org/W1506744765","https://openalex.org/W2944091050","https://openalex.org/W2998817056","https://openalex.org/W2904258669","https://openalex.org/W4313815718","https://openalex.org/W996380913","https://openalex.org/W2024084279","https://openalex.org/W2920185967","https://openalex.org/W2921837939"],"abstract_inverted_index":{"Rather":[0],"than":[1],"the":[2,29,33,49,70],"standard":[3],"practice":[4],"of":[5,28],"selecting":[6],"a":[7,12,20,25],"single":[8],"\"best-fit\"":[9],"distribution":[10,22],"from":[11],"candidate":[13,30],"set,":[14],"frequentist":[15],"model":[16],"averaging":[17],"(FMA)":[18],"forms":[19],"mixture":[21],"that":[23,46,63,76,88],"is":[24,89],"weighted":[26],"average":[27],"distributions":[31],"with":[32],"weights":[34],"tuned":[35],"by":[36],"cross-validation.":[37],"In":[38,58],"previous":[39],"work":[40],"we":[41,61],"showed":[42],"theoretically":[43],"and":[44,93],"empirically":[45],"FMA":[47,64,87],"in":[48,69],"probability":[50],"space":[51],"leads":[52],"to":[53,74,91],"higher":[54],"fidelity":[55],"input":[56],"distributions.":[57],"this":[59],"paper":[60],"show":[62],"can":[65],"also":[66,81],"be":[67],"implemented":[68],"quantile":[71],"space,":[72],"leading":[73],"fits":[75],"emphasize":[77],"tail":[78],"behavior.":[79],"We":[80],"describe":[82],"an":[83],"R":[84],"package":[85],"for":[86,95],"easy":[90],"use":[92],"available":[94],"download.":[96]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
