{"id":"https://openalex.org/W2635018027","doi":"https://doi.org/10.1007/s11222-018-9826-2","title":"Control variates for stochastic gradient MCMC","display_name":"Control variates for stochastic gradient MCMC","publication_year":2018,"publication_date":"2018-08-04","ids":{"openalex":"https://openalex.org/W2635018027","doi":"https://doi.org/10.1007/s11222-018-9826-2","mag":"2635018027"},"language":"en","primary_location":{"id":"doi:10.1007/s11222-018-9826-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-018-9826-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-018-9826-2.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":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11222-018-9826-2.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101778737","display_name":"Jack Baker","orcid":"https://orcid.org/0000-0003-0080-6875"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jack Baker","raw_affiliation_strings":["STOR-i Centre for Doctoral Training, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK"],"raw_orcid":"https://orcid.org/0000-0003-0080-6875","affiliations":[{"raw_affiliation_string":"STOR-i Centre for Doctoral Training, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085978090","display_name":"Paul Fearnhead","orcid":"https://orcid.org/0000-0002-9386-2341"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Paul Fearnhead","raw_affiliation_strings":["STOR-i Centre for Doctoral Training, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"STOR-i Centre for Doctoral Training, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068568859","display_name":"Emily B. Fox","orcid":"https://orcid.org/0000-0003-3188-9685"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]},{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Emily B. Fox","raw_affiliation_strings":["Department of Statistics, University of Washington, Seattle, WA, USA","Department of Statistics, University of Washington, Seattle, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"Department of Statistics, University of Washington, Seattle, USA","institution_ids":["https://openalex.org/I201448701","https://openalex.org/I58610484"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079845478","display_name":"Christopher Nemeth","orcid":"https://orcid.org/0000-0002-9084-3866"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Christopher Nemeth","raw_affiliation_strings":["STOR-i Centre for Doctoral Training, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"STOR-i Centre for Doctoral Training, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2090,"currency":"EUR","value_usd":2690},"apc_paid":{"value":2090,"currency":"EUR","value_usd":2690},"fwci":1.8307,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.86330192,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"29","issue":"3","first_page":"599","last_page":"615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":1.0,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":1.0,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9929999709129333,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9925000071525574,"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/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8504222631454468},{"id":"https://openalex.org/keywords/control-variates","display_name":"Control variates","score":0.7697409391403198},{"id":"https://openalex.org/keywords/variance-reduction","display_name":"Variance reduction","score":0.6870458722114563},{"id":"https://openalex.org/keywords/langevin-dynamics","display_name":"Langevin dynamics","score":0.6272491216659546},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5647481679916382},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.48668891191482544},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4417431056499481},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43079501390457153},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.38233691453933716},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3513984680175781},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3371310830116272},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.30757731199264526},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2902306318283081},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.1609988510608673}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8504222631454468},{"id":"https://openalex.org/C121683094","wikidata":"https://www.wikidata.org/wiki/Q3554721","display_name":"Control variates","level":5,"score":0.7697409391403198},{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.6870458722114563},{"id":"https://openalex.org/C2780004032","wikidata":"https://www.wikidata.org/wiki/Q6485978","display_name":"Langevin dynamics","level":2,"score":0.6272491216659546},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5647481679916382},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48668891191482544},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4417431056499481},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43079501390457153},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.38233691453933716},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3513984680175781},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3371310830116272},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.30757731199264526},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2902306318283081},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.1609988510608673},{"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":5,"locations":[{"id":"doi:10.1007/s11222-018-9826-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-018-9826-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-018-9826-2.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"},{"id":"pmh:oai:arXiv.org:1706.05439","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1706.05439","pdf_url":"https://arxiv.org/pdf/1706.05439","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:eprints.lancs.ac.uk:86844","is_oa":true,"landing_page_url":null,"pdf_url":"https://eprints.lancs.ac.uk/id/eprint/86844/2/1706.05439.pd.pdf","source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal Article"},{"id":"mag:2635018027","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1706.05439.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1706.05439","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1706.05439","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1007/s11222-018-9826-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-018-9826-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-018-9826-2.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":[{"id":"https://openalex.org/G1027573560","display_name":null,"funder_award_id":"IIS-1350133","funder_id":"https://openalex.org/F4320337389","funder_display_name":"Division of Information and Intelligent Systems"},{"id":"https://openalex.org/G1240511962","display_name":"CAREER: Scaling up Modeling and Statistical Inference for Massive Collections of Time Series","funder_award_id":"1350133","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1579515842","display_name":null,"funder_award_id":"EP/R018561","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G1904805678","display_name":null,"funder_award_id":"EP/R01860X/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G1974824942","display_name":null,"funder_award_id":"R018561","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G2011500132","display_name":null,"funder_award_id":"EP/K014463","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G2387017502","display_name":null,"funder_award_id":"IIS-1350133","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3245372514","display_name":null,"funder_award_id":"N00014-15-1-2380","funder_id":"https://openalex.org/F4320338298","funder_display_name":"Office of Naval Research Global"},{"id":"https://openalex.org/G3402893575","display_name":null,"funder_award_id":"EP/R018561/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4007862101","display_name":"Scalable and Exact Data Science for Security and Location-based Data","funder_award_id":"EP/S00159X/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4870096331","display_name":"New Approaches to Bayesian Data Science: Tackling Challenges from the Health Sciences","funder_award_id":"EP/R018561/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G5437064712","display_name":null,"funder_award_id":"N00014-15-1-2380","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G5914583752","display_name":"Data Science of the Natural Environment","funder_award_id":"EP/R01860X/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6890019585","display_name":"Intractable Likelihood: New Challenges from Modern Applications (ILike)","funder_award_id":"EP/K014463/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7164479687","display_name":"EPSRC CENTRE FOR DOCTORAL TRAINING IN STATISTICS & OPERATIONAL RESEARCH IN PARTNERSHIP WITH INDUSTRY (STOR-i)","funder_award_id":"EP/L015692/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7220767185","display_name":null,"funder_award_id":"EP/K014463/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"},{"id":"https://openalex.org/F4320337389","display_name":"Division of Information and Intelligent Systems","ror":"https://ror.org/053a2cp42"},{"id":"https://openalex.org/F4320338298","display_name":"Office of Naval Research Global","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2635018027.pdf","grobid_xml":"https://content.openalex.org/works/W2635018027.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W1535258871","https://openalex.org/W1599118604","https://openalex.org/W1880262756","https://openalex.org/W1983452151","https://openalex.org/W1992208280","https://openalex.org/W1993276303","https://openalex.org/W1994616650","https://openalex.org/W1998986742","https://openalex.org/W2015627422","https://openalex.org/W2029164135","https://openalex.org/W2035192392","https://openalex.org/W2101893470","https://openalex.org/W2110529144","https://openalex.org/W2137245235","https://openalex.org/W2165652770","https://openalex.org/W2167433878","https://openalex.org/W2263340393","https://openalex.org/W2478027467","https://openalex.org/W2552704617","https://openalex.org/W2615953416","https://openalex.org/W2950379384","https://openalex.org/W2951105989","https://openalex.org/W2963398635","https://openalex.org/W6600755281","https://openalex.org/W6601963440","https://openalex.org/W6659286983","https://openalex.org/W6813542777","https://openalex.org/W6845922490"],"related_works":["https://openalex.org/W2167433878","https://openalex.org/W2562674776","https://openalex.org/W3155455898","https://openalex.org/W1598072008","https://openalex.org/W2238129813","https://openalex.org/W3008835794","https://openalex.org/W3149189374","https://openalex.org/W2399207195","https://openalex.org/W2972371397","https://openalex.org/W2963977539","https://openalex.org/W3194546735","https://openalex.org/W2249115414","https://openalex.org/W2964102875","https://openalex.org/W3163241299","https://openalex.org/W3122049387","https://openalex.org/W2156964126","https://openalex.org/W2803585110","https://openalex.org/W2974054706","https://openalex.org/W1535087068","https://openalex.org/W2742287805"],"abstract_inverted_index":{"It":[0],"is":[1,25,129,191],"well":[2],"known":[3,143],"that":[4,61,138],"Markov":[5],"chain":[6],"Monte":[7],"Carlo":[8],"(MCMC)":[9],"methods":[10,20,31],"scale":[11],"poorly":[12],"with":[13,196],"dataset":[14,81,133],"size.":[15,82,134],"A":[16],"popular":[17,70],"class":[18],"of":[19,36,39,49,58,71,127,131,163,170,182],"for":[21,90,123,155],"solving":[22],"this":[23,106,195],"issue":[24],"stochastic":[26,62,91],"gradient":[27,38,63,88,92,107,181,199],"MCMC":[28,93,172],"(SGMCMC).":[29],"These":[30],"use":[32],"a":[33,56,124,139],"noisy":[34,198],"estimate":[35,89,200],"the":[37,40,44,50,68,80,100,116,119,132,161,164,168,171,180,183,188,197],"log-posterior,":[41],"which":[42,94],"reduces":[43],"per":[45],"iteration":[46],"computational":[47,76,120],"cost":[48,77,121],"algorithm.":[51],"Despite":[52],"this,":[53],"there":[54],"are":[55],"number":[57],"results":[59],"suggesting":[60],"Langevin":[64],"dynamics":[65],"(SGLD),":[66],"probably":[67],"most":[69],"these":[72],"methods,":[73],"still":[74],"has":[75],"proportional":[78],"to":[79,98,152],"We":[83,102],"suggest":[84],"an":[85],"alternative":[86],"log-posterior":[87],"uses":[95],"control":[96,147,176],"variates":[97,177],"reduce":[99],"variance.":[101],"analyse":[103],"SGLD":[104],"using":[105],"estimate,":[108],"and":[109],"show":[110,137],"that,":[111],"under":[112],"log-concavity":[113],"assumptions":[114],"on":[115,179],"target":[117],"distribution,":[118],"required":[122],"given":[125],"level":[126],"accuracy":[128],"independent":[130],"Next,":[135],"we":[136,185],"different":[140],"control-variate":[141],"technique,":[142],"as":[144],"zero":[145],"variance":[146,169,175,189],"variates,":[148],"can":[149],"be":[150],"applied":[151],"SGMCMC":[153],"algorithms":[154],"free.":[156],"This":[157],"postprocessing":[158],"step":[159],"improves":[160],"inference":[162],"algorithm":[165],"by":[166,193,202],"reducing":[167],"output.":[173],"Zero":[174],"rely":[178],"log-posterior;":[184],"explore":[186],"how":[187],"reduction":[190],"affected":[192],"replacing":[194],"calculated":[201],"SGMCMC.":[203]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}