{"id":"https://openalex.org/W4311034063","doi":"https://doi.org/10.1145/3550469.3555388","title":"Marginal Multiple Importance Sampling","display_name":"Marginal Multiple Importance Sampling","publication_year":2022,"publication_date":"2022-11-29","ids":{"openalex":"https://openalex.org/W4311034063","doi":"https://doi.org/10.1145/3550469.3555388"},"language":"en","primary_location":{"id":"doi:10.1145/3550469.3555388","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3550469.3555388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIGGRAPH Asia 2022 Conference Papers","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/A5029207620","display_name":"Rex West","orcid":"https://orcid.org/0009-0003-5457-8158"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Rex West","raw_affiliation_strings":["University of Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013165810","display_name":"Iliyan Georgiev","orcid":"https://orcid.org/0000-0002-9655-2138"},"institutions":[{"id":"https://openalex.org/I4210104522","display_name":"Autodesk (United Kingdom)","ror":"https://ror.org/01dzqde20","country_code":"GB","type":"company","lineage":["https://openalex.org/I1286353243","https://openalex.org/I4210104522"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Iliyan Georgiev","raw_affiliation_strings":["Autodesk, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Autodesk, United Kingdom","institution_ids":["https://openalex.org/I4210104522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040844821","display_name":"Toshiya Hachisuka","orcid":"https://orcid.org/0000-0003-0284-3776"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Toshiya Hachisuka","raw_affiliation_strings":["University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5029207620"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":1.1099,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.81904035,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9811000227928162,"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/T13126","display_name":"Scientific Research and Discoveries","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7076324224472046},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6867936253547668},{"id":"https://openalex.org/keywords/marginal-distribution","display_name":"Marginal distribution","score":0.6578696966171265},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5507109761238098},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5470585823059082},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5254033207893372},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4735680818557739},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.45990607142448425},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43341583013534546},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.42478153109550476},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3766227066516876},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.33398374915122986},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.18060389161109924},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.10141965746879578}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7076324224472046},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6867936253547668},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.6578696966171265},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5507109761238098},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5470585823059082},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5254033207893372},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4735680818557739},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.45990607142448425},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43341583013534546},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.42478153109550476},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3766227066516876},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33398374915122986},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.18060389161109924},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.10141965746879578},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3550469.3555388","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3550469.3555388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIGGRAPH Asia 2022 Conference Papers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.800000011920929,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1982871508","https://openalex.org/W2090346540","https://openalex.org/W2768744879","https://openalex.org/W3048397425","https://openalex.org/W4200456022","https://openalex.org/W4286611048"],"related_works":["https://openalex.org/W2137901802","https://openalex.org/W2541757757","https://openalex.org/W2139166181","https://openalex.org/W4238858747","https://openalex.org/W2135824962","https://openalex.org/W3126536330","https://openalex.org/W1995090402","https://openalex.org/W2015469788","https://openalex.org/W3103727361","https://openalex.org/W3082275435"],"abstract_inverted_index":{"Multiple":[0],"importance":[1],"sampling":[2,11,74],"(MIS)":[3],"is":[4,63],"a":[5,14,70,112],"powerful":[6],"tool":[7],"to":[8,53,64],"combine":[9,54,89],"different":[10],"techniques":[12,75,84,110],"in":[13],"provably":[15],"good":[16],"manner.":[17],"MIS":[18,52],"requires":[19],"that":[20],"the":[21,90,104],"techniques\u2019":[22],"probability":[23],"density":[24],"functions":[25],"(PDFs)":[26],"are":[27,43],"readily":[28,77],"evaluable":[29,78],"point-wise.":[30],"However,":[31],"this":[32],"requirement":[33],"may":[34],"not":[35],"be":[36],"satisfied":[37],"when":[38],"(some":[39],"of)":[40],"those":[41],"PDFs":[42],"marginals,":[44],"i.e.,":[45],"integrals":[46],"of":[47,73,107,121],"other":[48],"PDFs.":[49,59,80],"We":[50,81],"generalize":[51],"samples":[55,91],"from":[56,85,93],"such":[57],"marginal":[58,114],"The":[60],"key":[61],"idea":[62],"consider":[65],"each":[66],"marginalization":[67],"domain":[68],"as":[69],"continuous":[71],"space":[72],"with":[76,103],"(conditional)":[79],"stochastically":[82],"select":[83],"these":[86],"spaces":[87],"and":[88],"drawn":[92],"them":[94],"into":[95],"an":[96],"unbiased":[97],"estimator.":[98],"Prior":[99],"work":[100],"has":[101],"dealt":[102],"special":[105],"cases":[106],"multiple":[108],"classical":[109],"or":[111],"single":[113],"one.":[115],"Our":[116],"formulation":[117],"can":[118],"handle":[119],"mixtures":[120],"those.":[122]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}
