{"id":"https://openalex.org/W2105569158","doi":"https://doi.org/10.1017/s0021900200113440","title":"Nonasymptotic performance analysis of importance sampling schemes for small noise diffusions","display_name":"Nonasymptotic performance analysis of importance sampling schemes for small noise diffusions","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2105569158","doi":"https://doi.org/10.1017/s0021900200113440","mag":"2105569158"},"language":"en","primary_location":{"id":"doi:10.1017/s0021900200113440","is_oa":false,"landing_page_url":"https://doi.org/10.1017/s0021900200113440","pdf_url":null,"source":{"id":"https://openalex.org/S160779208","display_name":"Journal of Applied Probability","issn_l":"0021-9002","issn":["0021-9002","1475-6072"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Applied Probability","raw_type":"journal-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/A5103169334","display_name":"Konstantinos Spiliopoulos","orcid":"https://orcid.org/0000-0003-2021-6426"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Konstantinos Spiliopoulos","raw_affiliation_strings":["Boston University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5103169334"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8231,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78751645,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"52","issue":"03","first_page":"797","last_page":"810"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11720","display_name":"Probability and Risk Models","score":0.9994000196456909,"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":0.9994000196456909,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9921000003814697,"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/T11152","display_name":"Stochastic processes and statistical mechanics","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"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/mathematics","display_name":"Mathematics","score":0.7948856353759766},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7549509406089783},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.7504541873931885},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6304856538772583},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.5694456100463867},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.559587836265564},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5459399223327637},{"id":"https://openalex.org/keywords/second-moment-of-area","display_name":"Second moment of area","score":0.5454909801483154},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.49622684717178345},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.47118476033210754},{"id":"https://openalex.org/keywords/asymptotic-expansion","display_name":"Asymptotic expansion","score":0.4333687424659729},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.36362528800964355},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.30114445090293884},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.278075248003006},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.11952510476112366}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7948856353759766},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7549509406089783},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.7504541873931885},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6304856538772583},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5694456100463867},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.559587836265564},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5459399223327637},{"id":"https://openalex.org/C61062188","wikidata":"https://www.wikidata.org/wiki/Q835065","display_name":"Second moment of area","level":2,"score":0.5454909801483154},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.49622684717178345},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.47118476033210754},{"id":"https://openalex.org/C11683690","wikidata":"https://www.wikidata.org/wiki/Q752726","display_name":"Asymptotic expansion","level":2,"score":0.4333687424659729},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.36362528800964355},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.30114445090293884},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.278075248003006},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.11952510476112366},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"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/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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":2,"locations":[{"id":"doi:10.1017/s0021900200113440","is_oa":false,"landing_page_url":"https://doi.org/10.1017/s0021900200113440","pdf_url":null,"source":{"id":"https://openalex.org/S160779208","display_name":"Journal of Applied Probability","issn_l":"0021-9002","issn":["0021-9002","1475-6072"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Applied Probability","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.742.2750","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.742.2750","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1310.6928.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.6600000262260437}],"awards":[{"id":"https://openalex.org/G3279812375","display_name":"Monte Carlo Methods, Metastability and Stochastic Processes with Multiple Scales","funder_award_id":"1312124","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1938578607","https://openalex.org/W1985912219","https://openalex.org/W2000521974","https://openalex.org/W2022386023","https://openalex.org/W2027477535","https://openalex.org/W2028622970","https://openalex.org/W2036213650","https://openalex.org/W2061071876","https://openalex.org/W2110157793","https://openalex.org/W2130185378","https://openalex.org/W2162407085","https://openalex.org/W3098867282","https://openalex.org/W3099324675","https://openalex.org/W3099598024","https://openalex.org/W3101742508","https://openalex.org/W4232187485"],"related_works":["https://openalex.org/W2966643660","https://openalex.org/W2112927832","https://openalex.org/W4287375276","https://openalex.org/W3173152589","https://openalex.org/W3121253862","https://openalex.org/W2079603970","https://openalex.org/W4309953713","https://openalex.org/W2049530973","https://openalex.org/W1543030417","https://openalex.org/W2007293417"],"abstract_inverted_index":{"In":[0,21],"this":[1],"paper":[2],"we":[3,43,61,109],"develop":[4],"a":[5,38,56,64,78,97,128],"prelimit":[6],"analysis":[7],"of":[8,26,29,41,48,73,92,114,118],"performance":[9,25,117],"measures":[10],"for":[11,127],"importance":[12,22,122,134],"sampling":[13,23,123,135],"schemes":[14,124],"related":[15],"to":[16,55,70,88],"small":[17],"noise":[18,75],"diffusion":[19],"processes.":[20],"the":[24,45,49,53,71,74,89,93,112],"any":[27],"change":[28,40],"measure":[30],"is":[31],"characterized":[32],"by":[33],"its":[34,82],"second":[35,46,94],"moment.":[36],"For":[37],"given":[39],"measure,":[42],"characterize":[44],"moment":[47,95],"corresponding":[50],"estimator":[51],"as":[52],"solution":[54],"partial":[57],"differential":[58],"equation,":[59],"which":[60],"analyze":[62],"via":[63],"full":[65],"asymptotic":[66,106],"expansion":[67,107],"with":[68],"respect":[69],"size":[72],"and":[76,125],"obtain":[77,110],"precise":[79],"statement":[80],"on":[81],"accuracy.":[83],"The":[84,105],"main":[85],"correction":[86],"term":[87],"decay":[90],"rate":[91],"solves":[96],"transport":[98],"equation":[99],"that":[100,108],"can":[101],"be":[102],"solved":[103],"explicitly.":[104],"identifies":[111],"source":[113],"possible":[115],"poor":[116],"nevertheless":[119],"asymptotically":[120],"optimal":[121],"allows":[126],"more":[129],"accurate":[130],"comparison":[131],"among":[132],"competing":[133],"schemes.":[136]},"counts_by_year":[{"year":2017,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
