{"id":"https://openalex.org/W2003327131","doi":"https://doi.org/10.1145/1190095.1190121","title":"Splitting with weight windows to control the likelihood ratio in importance sampling","display_name":"Splitting with weight windows to control the likelihood ratio in importance sampling","publication_year":2006,"publication_date":"2006-01-01","ids":{"openalex":"https://openalex.org/W2003327131","doi":"https://doi.org/10.1145/1190095.1190121","mag":"2003327131"},"language":"en","primary_location":{"id":"doi:10.1145/1190095.1190121","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1190095.1190121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st international conference on Performance evaluation methodolgies and tools  - valuetools '06","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/A5091510633","display_name":"Pierre L\u2019Ecuyer","orcid":"https://orcid.org/0000-0002-3184-0796"},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Pierre L'Ecuyer","raw_affiliation_strings":["Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, Canada","Universit\u00e9 de Montr\u00e9al Montr\u00e9al, Canada"],"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, Canada","institution_ids":["https://openalex.org/I70931966"]},{"raw_affiliation_string":"Universit\u00e9 de Montr\u00e9al Montr\u00e9al, Canada","institution_ids":["https://openalex.org/I70931966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079962386","display_name":"Bruno Tuffin","orcid":"https://orcid.org/0000-0001-9415-1130"},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I2802519937","display_name":"Institut de Recherche en Informatique et Syst\u00e8mes Al\u00e9atoires","ror":"https://ror.org/00myn0z94","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I205703379","https://openalex.org/I2802204017","https://openalex.org/I2802519937","https://openalex.org/I28221208","https://openalex.org/I4210127572","https://openalex.org/I4210159245","https://openalex.org/I56067802"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Bruno Tuffin","raw_affiliation_strings":["IRISA-INRIA, Rennes Cedex, France","IRISA--INRIA, Rennes Cedex, France"],"affiliations":[{"raw_affiliation_string":"IRISA-INRIA, Rennes Cedex, France","institution_ids":["https://openalex.org/I2802519937","https://openalex.org/I1326498283"]},{"raw_affiliation_string":"IRISA--INRIA, Rennes Cedex, France","institution_ids":["https://openalex.org/I1326498283"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5091510633"],"corresponding_institution_ids":["https://openalex.org/I70931966"],"apc_list":null,"apc_paid":null,"fwci":1.2637,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.80593827,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"21","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11720","display_name":"Probability and Risk Models","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11720","display_name":"Probability and Risk Models","score":0.9998999834060669,"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/T12011","display_name":"Insurance, Mortality, Demography, Risk Management","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9803000092506409,"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/rare-events","display_name":"Rare events","score":0.7837955951690674},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6988575458526611},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.666993260383606},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6520609855651855},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5739491581916809},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5552279949188232},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5433389544487},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.5273241400718689},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.46211034059524536},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.43439850211143494},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3373282551765442},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3249874711036682},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.3142295181751251},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.20015490055084229}],"concepts":[{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.7837955951690674},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6988575458526611},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.666993260383606},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6520609855651855},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5739491581916809},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5552279949188232},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5433389544487},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.5273241400718689},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.46211034059524536},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.43439850211143494},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3373282551765442},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3249874711036682},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.3142295181751251},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.20015490055084229},{"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/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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1190095.1190121","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1190095.1190121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st international conference on Performance evaluation methodolgies and tools  - valuetools '06","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.73.3970","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.73.3970","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.iro.umontreal.ca/~lecuyer/myftp/papers/valuetools06.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320994","display_name":"Canada Research Chairs","ror":"https://ror.org/0517h6h17"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W70896419","https://openalex.org/W201155865","https://openalex.org/W277831955","https://openalex.org/W599484449","https://openalex.org/W1493527860","https://openalex.org/W1505324634","https://openalex.org/W1553168519","https://openalex.org/W1621653743","https://openalex.org/W1981918347","https://openalex.org/W2035017139","https://openalex.org/W2035155944","https://openalex.org/W2070406436","https://openalex.org/W2071157273","https://openalex.org/W2086374769","https://openalex.org/W2087018957","https://openalex.org/W2095662822","https://openalex.org/W2097511294","https://openalex.org/W2105099705","https://openalex.org/W2117299406","https://openalex.org/W2146416787","https://openalex.org/W2149228262","https://openalex.org/W2154151239","https://openalex.org/W2164384055","https://openalex.org/W2164863330"],"related_works":["https://openalex.org/W2013921918","https://openalex.org/W2019724159","https://openalex.org/W4280508323","https://openalex.org/W1964876911","https://openalex.org/W2160389399","https://openalex.org/W4376653316","https://openalex.org/W2116791275","https://openalex.org/W2246249754","https://openalex.org/W2371672232","https://openalex.org/W1499907193"],"abstract_inverted_index":{"Importance":[0],"sampling":[1],"(IS)":[2],"is":[3,25],"the":[4,16,22,26,33,43,51,54,64,67,96,113,118],"most":[5],"widely":[6],"used":[7],"efficiency":[8,52],"improvement":[9],"method":[10],"for":[11],"rare-event":[12],"simulation.":[13],"When":[14],"estimating":[15],"probability":[17,65],"of":[18,28,45,53,66],"a":[19,39],"rare":[20,34,68],"event,":[21],"IS":[23,55],"estimator":[24,56],"product":[27],"an":[29],"indicator":[30],"function":[31],"(that":[32],"event":[35,69],"has":[36],"occurred)":[37],"by":[38,98],"likelihood":[40,47],"ratio.":[41],"Reducing":[42],"variance":[44],"that":[46],"ratio":[48],"can":[49,88,103,115],"increase":[50],"if":[57],"(a)":[58],"this":[59,74,82,87],"does":[60,75],"not":[61,76],"reduce":[62],"significantly":[63],"under":[70],"IS,":[71],"and":[72,94],"(b)":[73],"require":[77],"much":[78],"more":[79],"work.":[80],"In":[81],"paper,":[83],"we":[84],"explain":[85],"how":[86,112],"be":[89,104],"achieved":[90],"via":[91],"weight":[92,119],"windows":[93,120],"illustrate":[95],"idea":[97],"numerical":[99],"examples.":[100],"The":[101],"savings":[102],"large":[105],"in":[106],"some":[107],"situations.":[108],"We":[109],"also":[110],"show":[111],"technique":[114],"backlash":[116],"when":[117],"are":[121],"wrongly":[122],"selected.":[123]},"counts_by_year":[{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}