{"id":"https://openalex.org/W3212540513","doi":"https://doi.org/10.1109/mlsp52302.2021.9596301","title":"Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms","display_name":"Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W3212540513","doi":"https://doi.org/10.1109/mlsp52302.2021.9596301","mag":"3212540513"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp52302.2021.9596301","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596301","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.aalto.fi/en/publications/7191685f-7c4e-4b6a-b0ab-ff5c8d59efbb","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075994876","display_name":"Simo S\u00e4rkk\u00e4","orcid":"https://orcid.org/0000-0002-7031-9354"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Simo Sarkka","raw_affiliation_strings":["Aalto University, Finland"],"affiliations":[{"raw_affiliation_string":"Aalto University, Finland","institution_ids":["https://openalex.org/I9927081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066349434","display_name":"Christos Merkatas","orcid":"https://orcid.org/0000-0001-7880-7443"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Christos Merkatas","raw_affiliation_strings":["Aalto University, Finland"],"affiliations":[{"raw_affiliation_string":"Aalto University, Finland","institution_ids":["https://openalex.org/I9927081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033957157","display_name":"Toni Karvonen","orcid":"https://orcid.org/0000-0002-5984-7295"},"institutions":[{"id":"https://openalex.org/I4210128584","display_name":"The Alan Turing Institute","ror":"https://ror.org/035dkdb55","country_code":"GB","type":"facility","lineage":["https://openalex.org/I4210128584"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Toni Karvonen","raw_affiliation_strings":["Alan Turing Institute, London, UK"],"affiliations":[{"raw_affiliation_string":"Alan Turing Institute, London, UK","institution_ids":["https://openalex.org/I4210128584"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5075994876"],"corresponding_institution_ids":["https://openalex.org/I9927081"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16706201,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9977999925613403,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.996999979019165,"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":0.8073931932449341},{"id":"https://openalex.org/keywords/metropolis\u2013hastings-algorithm","display_name":"Metropolis\u2013Hastings algorithm","score":0.696811854839325},{"id":"https://openalex.org/keywords/stochastic-differential-equation","display_name":"Stochastic differential equation","score":0.6528306007385254},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5540345907211304},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.49508848786354065},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.48792144656181335},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4856073260307312},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4638274908065796},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4603310823440552},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.46023866534233093},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4404072165489197},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3587471842765808},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.3484223186969757},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17395281791687012},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11947527527809143},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1164868175983429},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.08971822261810303}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8073931932449341},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.696811854839325},{"id":"https://openalex.org/C51955184","wikidata":"https://www.wikidata.org/wiki/Q1545585","display_name":"Stochastic differential equation","level":2,"score":0.6528306007385254},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5540345907211304},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.49508848786354065},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.48792144656181335},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4856073260307312},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4638274908065796},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4603310823440552},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.46023866534233093},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4404072165489197},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3587471842765808},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3484223186969757},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17395281791687012},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11947527527809143},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1164868175983429},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.08971822261810303},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mlsp52302.2021.9596301","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596301","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:aaltodoc.aalto.fi:123456789/112553","is_oa":true,"landing_page_url":"https://research.aalto.fi/en/publications/7191685f-7c4e-4b6a-b0ab-ff5c8d59efbb","pdf_url":null,"source":{"id":"https://openalex.org/S4306401662","display_name":"Aaltodoc (Aalto University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I9927081","host_organization_name":"Aalto University","host_organization_lineage":["https://openalex.org/I9927081"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"acceptedVersion"}],"best_oa_location":{"id":"pmh:oai:aaltodoc.aalto.fi:123456789/112553","is_oa":true,"landing_page_url":"https://research.aalto.fi/en/publications/7191685f-7c4e-4b6a-b0ab-ff5c8d59efbb","pdf_url":null,"source":{"id":"https://openalex.org/S4306401662","display_name":"Aaltodoc (Aalto University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I9927081","host_organization_name":"Aalto University","host_organization_lineage":["https://openalex.org/I9927081"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"acceptedVersion"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6100000143051147,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W316798226","https://openalex.org/W621546036","https://openalex.org/W1545319692","https://openalex.org/W1567512734","https://openalex.org/W1983452151","https://openalex.org/W2045656233","https://openalex.org/W2056760934","https://openalex.org/W2059448777","https://openalex.org/W2087435860","https://openalex.org/W2138309709","https://openalex.org/W2148534890","https://openalex.org/W2164770369","https://openalex.org/W2478027467","https://openalex.org/W2940934113","https://openalex.org/W2954040150","https://openalex.org/W3093931768","https://openalex.org/W4240790602","https://openalex.org/W4248681815"],"related_works":["https://openalex.org/W2956835927","https://openalex.org/W2392396032","https://openalex.org/W2071668645","https://openalex.org/W4250051631","https://openalex.org/W160986626","https://openalex.org/W2073412813","https://openalex.org/W4362705882","https://openalex.org/W4390532044","https://openalex.org/W217235565","https://openalex.org/W3087071515"],"abstract_inverted_index":{"Markov":[0],"chain":[1],"Monte":[2],"Carlo":[3],"(MCMC)":[4],"methods":[5],"are":[6],"a":[7,30,53,61],"cornerstone":[8],"of":[9,29,63,75,80],"Bayesian":[10],"inference":[11],"and":[12,47,87],"stochastic":[13,31],"simulation.":[14],"The":[15,78],"Metropolis-adjusted":[16],"Langevin":[17],"algorithm":[18,46,82],"(MALA)":[19],"is":[20,38,83],"an":[21],"MCMC":[22],"method":[23],"that":[24],"relies":[25],"on":[26,85],"the":[27,39,44,64,73,76,81],"simulation":[28,50],"differential":[32],"equation":[33],"(SDE)":[34],"whose":[35],"stationary":[36],"distribution":[37],"desired":[40],"target":[41],"density":[42,70],"using":[43,52],"Euler-Maruyama":[45],"accounts":[48],"for":[49,72],"errors":[51],"Metropolis":[54],"step.":[55],"In":[56],"this":[57],"paper":[58],"we":[59],"propose":[60],"modification":[62],"MALA":[65],"which":[66],"uses":[67],"Gaussian":[68],"assumed":[69],"approximations":[71],"integration":[74],"SDE.":[77],"effectiveness":[79],"illustrated":[84],"simulated":[86],"real":[88],"data":[89],"sets.":[90]},"counts_by_year":[],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
