{"id":"https://openalex.org/W4251408925","doi":"https://doi.org/10.1017/s0021900200011645","title":"Automated State-Dependent Importance Sampling for Markov Jump Processes via Sampling from the Zero-Variance Distribution","display_name":"Automated State-Dependent Importance Sampling for Markov Jump Processes via Sampling from the Zero-Variance Distribution","publication_year":2014,"publication_date":"2014-09-01","ids":{"openalex":"https://openalex.org/W4251408925","doi":"https://doi.org/10.1017/s0021900200011645"},"language":"en","primary_location":{"id":"doi:10.1017/s0021900200011645","is_oa":false,"landing_page_url":"http://doi.org/10.1017/s0021900200011645","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/A5028600801","display_name":"Adam W. Grace","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Adam W. Grace","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088935199","display_name":"Dirk P. Kroese","orcid":"https://orcid.org/0000-0003-2281-9431"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dirk P. Kroese","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5012212299","display_name":"Werner Sandmann","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Werner Sandmann","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028600801"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46974484,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"51","issue":"03","first_page":"741","last_page":"755"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11720","display_name":"Probability and Risk Models","score":0.9995999932289124,"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":0.9995999932289124,"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/T11152","display_name":"Stochastic processes and statistical mechanics","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.991100013256073,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5835686326026917},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5817542672157288},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5688055157661438},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5252760648727417},{"id":"https://openalex.org/keywords/jump","display_name":"Jump","score":0.5209051966667175},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5105267763137817},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.4833347499370575},{"id":"https://openalex.org/keywords/rare-events","display_name":"Rare events","score":0.44805431365966797},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4393320083618164},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.4236883223056793},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.4191698133945465},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.418794184923172},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.4155437648296356},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4014953076839447},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.3786769509315491},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.37207040190696716},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3618885278701782},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3015737235546112},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.293156236410141}],"concepts":[{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5835686326026917},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5817542672157288},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5688055157661438},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5252760648727417},{"id":"https://openalex.org/C2780695682","wikidata":"https://www.wikidata.org/wiki/Q4005959","display_name":"Jump","level":2,"score":0.5209051966667175},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5105267763137817},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.4833347499370575},{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.44805431365966797},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4393320083618164},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.4236883223056793},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.4191698133945465},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.418794184923172},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.4155437648296356},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4014953076839447},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.3786769509315491},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.37207040190696716},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3618885278701782},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3015737235546112},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.293156236410141},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1017/s0021900200011645","is_oa":false,"landing_page_url":"http://doi.org/10.1017/s0021900200011645","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1975305169","https://openalex.org/W1983663927","https://openalex.org/W2027096901","https://openalex.org/W2027181221","https://openalex.org/W2062398463","https://openalex.org/W2063986634","https://openalex.org/W2073148112","https://openalex.org/W2078049402","https://openalex.org/W2078119846","https://openalex.org/W2078923597","https://openalex.org/W2082293508","https://openalex.org/W2087018957","https://openalex.org/W2090590416","https://openalex.org/W2090951154","https://openalex.org/W2116030639","https://openalex.org/W2125299871","https://openalex.org/W2146416787","https://openalex.org/W2155418451","https://openalex.org/W3100817765"],"related_works":["https://openalex.org/W2160389399","https://openalex.org/W2130519334","https://openalex.org/W2122865681","https://openalex.org/W2362249705","https://openalex.org/W2127967590","https://openalex.org/W2004707959","https://openalex.org/W2116791275","https://openalex.org/W2156223142","https://openalex.org/W2001848173","https://openalex.org/W4298279785"],"abstract_inverted_index":{"Many":[0],"complex":[1],"systems":[2],"can":[3,24],"be":[4,25,46],"modeled":[5],"via":[6],"Markov":[7,41,70,91],"jump":[8,42,92],"processes.":[9],"Applications":[10],"include":[11],"chemical":[12,121],"reactions,":[13],"population":[14],"dynamics,":[15],"and":[16,27,68,94,119],"telecommunication":[17],"networks.":[18],"Rare-event":[19],"estimation":[20],"for":[21],"such":[22],"models":[23],"difficult":[26],"is":[28,66,84],"often":[29],"computationally":[30],"expensive,":[31],"because":[32],"typically":[33],"many":[34],"(or":[35],"very":[36],"long)":[37],"paths":[38],"of":[39,90],"the":[40,52,77,106],"process":[43],"need":[44],"to":[45,50,62,74,86,104],"simulated":[47],"in":[48,116],"order":[49],"observe":[51],"rare":[53],"event.":[54],"We":[55,109],"present":[56],"a":[57,87,101],"state-dependent":[58],"importance":[59,79,107],"sampling":[60,80],"approach":[61],"this":[63],"problem":[64],"that":[65],"adaptive":[67],"uses":[69],"chain":[71],"Monte":[72],"Carlo":[73],"sample":[75,103],"from":[76],"zero-variance":[78],"distribution.":[81],"The":[82],"method":[83],"applicable":[85],"wide":[88],"range":[89],"processes":[93],"achieves":[95],"high":[96],"accuracy,":[97],"while":[98],"requiring":[99],"only":[100],"small":[102],"obtain":[105],"parameters.":[108],"demonstrate":[110],"its":[111],"efficiency":[112],"through":[113],"benchmark":[114],"examples":[115],"queueing":[117],"theory":[118],"stochastic":[120],"kinetics.":[122]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}