{"id":"https://openalex.org/W4415378510","doi":"https://doi.org/10.1007/s11222-025-10741-4","title":"A non-asymptotic error analysis for parallel Monte Carlo estimation from many short Markov chains","display_name":"A non-asymptotic error analysis for parallel Monte Carlo estimation from many short Markov chains","publication_year":2025,"publication_date":"2025-10-21","ids":{"openalex":"https://openalex.org/W4415378510","doi":"https://doi.org/10.1007/s11222-025-10741-4"},"language":"en","primary_location":{"id":"doi:10.1007/s11222-025-10741-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-025-10741-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-025-10741-4.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11222-025-10741-4.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103015265","display_name":"Austin Brown","orcid":"https://orcid.org/0000-0003-1576-8381"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Austin Brown","raw_affiliation_strings":["Department of Statistics, Texas A&M University, College Station, Texas, USA"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Texas A&M University, College Station, Texas, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5103015265"],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":{"value":2090,"currency":"EUR","value_usd":2690},"apc_paid":{"value":2090,"currency":"EUR","value_usd":2690},"fwci":2.861,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.9263086,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"35","issue":"6","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9983999729156494,"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"}},{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9976999759674072,"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"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9846000075340271,"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/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8307999968528748},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.7310000061988831},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.5900999903678894},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5515000224113464},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.5422999858856201},{"id":"https://openalex.org/keywords/markov-chain-mixing-time","display_name":"Markov chain mixing time","score":0.5102999806404114},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4668000042438507},{"id":"https://openalex.org/keywords/markov-property","display_name":"Markov property","score":0.46470001339912415},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.46000000834465027}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8307999968528748},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.7310000061988831},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5900999903678894},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5515000224113464},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.5422999858856201},{"id":"https://openalex.org/C97074811","wikidata":"https://www.wikidata.org/wiki/Q6771322","display_name":"Markov chain mixing time","level":5,"score":0.5102999806404114},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5048999786376953},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4668000042438507},{"id":"https://openalex.org/C189973286","wikidata":"https://www.wikidata.org/wiki/Q176695","display_name":"Markov property","level":4,"score":0.46470001339912415},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.46000000834465027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45190000534057617},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.43950000405311584},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43369999527931213},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.42170000076293945},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.41749998927116394},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3955000042915344},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.34130001068115234},{"id":"https://openalex.org/C106666656","wikidata":"https://www.wikidata.org/wiki/Q6771323","display_name":"Markov kernel","level":5,"score":0.31790000200271606},{"id":"https://openalex.org/C196455857","wikidata":"https://www.wikidata.org/wiki/Q5473264","display_name":"Forward algorithm","level":5,"score":0.30329999327659607},{"id":"https://openalex.org/C187653413","wikidata":"https://www.wikidata.org/wiki/Q7135015","display_name":"Parallel tempering","level":5,"score":0.28999999165534973},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.27880001068115234},{"id":"https://openalex.org/C147080032","wikidata":"https://www.wikidata.org/wiki/Q176720","display_name":"Continuous-time Markov chain","level":5,"score":0.2639999985694885},{"id":"https://openalex.org/C187192777","wikidata":"https://www.wikidata.org/wiki/Q381699","display_name":"Rejection sampling","level":5,"score":0.263700008392334},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2623000144958496},{"id":"https://openalex.org/C163540672","wikidata":"https://www.wikidata.org/wiki/Q1115431","display_name":"Markov renewal process","level":5,"score":0.25270000100135803},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s11222-025-10741-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-025-10741-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-025-10741-4.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s11222-025-10741-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-025-10741-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-025-10741-4.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415378510.pdf","grobid_xml":"https://content.openalex.org/works/W4415378510.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1895076743","https://openalex.org/W1988698355","https://openalex.org/W1989164753","https://openalex.org/W2006343645","https://openalex.org/W2017170340","https://openalex.org/W2037468546","https://openalex.org/W2039765474","https://openalex.org/W2046185491","https://openalex.org/W2074711122","https://openalex.org/W2099878672","https://openalex.org/W2110097348","https://openalex.org/W2148534890","https://openalex.org/W2162340617","https://openalex.org/W2484406420","https://openalex.org/W2575742565","https://openalex.org/W2963625110","https://openalex.org/W3013998694","https://openalex.org/W3022264239","https://openalex.org/W4233413206","https://openalex.org/W4379387290","https://openalex.org/W4401180887"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Single-chain":[1],"Markov":[2,10,52,65,107,128],"chain":[3,11,66,129],"Monte":[4,41],"Carlo":[5,42],"simulates":[6],"realizations":[7],"from":[8,51,84],"a":[9,38,55,79,94,106,132],"to":[12,96,159],"estimate":[13],"expectations":[14],"with":[15],"the":[16,63,88,116,127,151],"empirical":[17],"average.":[18],"The":[19,59],"single-chain":[20],"simulation":[21],"is":[22,62,93,113,144],"generally":[23],"of":[24,31,54,136],"considerable":[25],"length":[26],"and":[27,71,122,139,150],"restricts":[28],"many":[29],"advantages":[30],"modern":[32],"parallel":[33],"computation.":[34],"This":[35],"paper":[36],"constructs":[37],"novel":[39],"many-short-chains":[40],"(MSC)":[43],"estimator":[44,90,118],"by":[45,103],"averaging":[46],"over":[47],"multiple":[48],"independent":[49,64],"sums":[50],"chains":[53],"guaranteed":[56],"short":[57],"length.":[58],"computational":[60],"advantage":[61],"simulations":[67],"can":[68],"be":[69,73],"fast":[70],"may":[72],"run":[74],"in":[75,100],"parallel,":[76],"but":[77],"require":[78],"well-designed":[80],"initial":[81],"distribution":[82],"constructed":[83],"importance":[85,101],"sampling.":[86],"Alternatively,":[87],"MSC":[89,117],"introduced":[91],"here":[92],"method":[95],"improve":[97],"estimation":[98,135],"properties":[99],"sampling":[102],"additionally":[104],"simulating":[105],"chain.":[108],"A":[109],"non-asymptotic":[110],"error":[111],"analysis":[112],"developed":[114],"for":[115,134,155],"under":[119],"both":[120],"geometric":[121],"multiplicative":[123],"drift":[124],"conditions":[125],"on":[126,146],"that":[130],"allows":[131],"theory":[133],"highly":[137],"irregular":[138],"unbounded":[140],"functions.":[141],"Empirical":[142],"performance":[143],"illustrated":[145],"an":[147],"autoregressive":[148],"process":[149],"P\u00f3lya-Gamma":[152],"Gibbs":[153],"sampler":[154],"Bayesian":[156],"logistic":[157],"regression":[158],"predict":[160],"cardiovascular":[161],"disease.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-11T06:11:40.159057","created_date":"2025-10-21T00:00:00"}
