{"id":"https://openalex.org/W2990385211","doi":"https://doi.org/10.1137/19m1303162","title":"Fokker--Planck Particle Systems for Bayesian Inference: Computational Approaches","display_name":"Fokker--Planck Particle Systems for Bayesian Inference: Computational Approaches","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W2990385211","doi":"https://doi.org/10.1137/19m1303162","mag":"2990385211"},"language":"en","primary_location":{"id":"doi:10.1137/19m1303162","is_oa":false,"landing_page_url":"https://doi.org/10.1137/19m1303162","pdf_url":null,"source":{"id":"https://openalex.org/S2911293512","display_name":"SIAM/ASA Journal on Uncertainty Quantification","issn_l":"2166-2525","issn":["2166-2525"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM/ASA Journal on Uncertainty Quantification","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1911.10832","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020753959","display_name":"Sebastian Reich","orcid":"https://orcid.org/0000-0002-5336-8904"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sebastian Reich","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-5336-8904","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5074701373","display_name":"Simon Wei\u00dfmann","orcid":"https://orcid.org/0000-0002-5111-6658"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Simon Weissmann","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-5111-6658","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4292,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.61321735,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"9","issue":"2","first_page":"446","last_page":"482"},"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":0.9994999766349792,"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":0.9994999766349792,"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":0.9986000061035156,"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/T12261","display_name":"Statistical Mechanics and Entropy","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.5360119342803955},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.46668893098831177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4537988603115082},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.44852393865585327},{"id":"https://openalex.org/keywords/ensemble-kalman-filter","display_name":"Ensemble Kalman filter","score":0.4415222704410553},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.439167320728302},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.42480647563934326},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.42369112372398376},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.4180895984172821},{"id":"https://openalex.org/keywords/brownian-motion","display_name":"Brownian motion","score":0.4164943993091583},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.38078370690345764},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.3719993233680725},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3707020580768585},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3381076455116272},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3362787067890167},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.27607637643814087},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22731435298919678},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.19323298335075378}],"concepts":[{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.5360119342803955},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.46668893098831177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4537988603115082},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.44852393865585327},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.4415222704410553},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.439167320728302},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.42480647563934326},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.42369112372398376},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.4180895984172821},{"id":"https://openalex.org/C112401455","wikidata":"https://www.wikidata.org/wiki/Q178036","display_name":"Brownian motion","level":2,"score":0.4164943993091583},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.38078370690345764},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3719993233680725},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3707020580768585},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3381076455116272},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3362787067890167},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.27607637643814087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22731435298919678},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.19323298335075378},{"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1137/19m1303162","is_oa":false,"landing_page_url":"https://doi.org/10.1137/19m1303162","pdf_url":null,"source":{"id":"https://openalex.org/S2911293512","display_name":"SIAM/ASA Journal on Uncertainty Quantification","issn_l":"2166-2525","issn":["2166-2525"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM/ASA Journal on Uncertainty Quantification","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1911.10832","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.10832","pdf_url":"https://arxiv.org/pdf/1911.10832","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"raw_type":"text"},{"id":"mag:2990385211","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1911.10832","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"pmh:oai:kobv.de-opus4-uni-potsdam:61550","is_oa":false,"landing_page_url":"https://publishup.uni-potsdam.de/frontdoor/index/index/docId/61550","pdf_url":null,"source":{"id":"https://openalex.org/S4377196346","display_name":"publish.UP (University of Potsdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I176453806","host_organization_name":"University of Potsdam","host_organization_lineage":["https://openalex.org/I176453806"],"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":"article"},{"id":"doi:10.48550/arxiv.1911.10832","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1911.10832","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"pmh:oai:arXiv.org:1911.10832","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.10832","pdf_url":"https://arxiv.org/pdf/1911.10832","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2601165318","display_name":null,"funder_award_id":"SFB 1294","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G4092581361","display_name":"SFB 1294: Datenassimilation \u2013 Die nahtlose Verschmelzung von Daten und Modellen","funder_award_id":"318763901","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G630736540","display_name":null,"funder_award_id":"RTG 1953","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320310414","display_name":"Baden-W\u00fcrttemberg Stiftung","ror":"https://ror.org/031h5fa94"},{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2990385211.pdf","grobid_xml":"https://content.openalex.org/works/W2990385211.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1029488735","https://openalex.org/W1522319528","https://openalex.org/W1531455566","https://openalex.org/W1545319692","https://openalex.org/W1571975558","https://openalex.org/W1921511217","https://openalex.org/W1973396249","https://openalex.org/W1992957154","https://openalex.org/W2004247736","https://openalex.org/W2006028648","https://openalex.org/W2009104157","https://openalex.org/W2030911724","https://openalex.org/W2071048859","https://openalex.org/W2093943787","https://openalex.org/W2101687185","https://openalex.org/W2119668864","https://openalex.org/W2143534834","https://openalex.org/W2147357149","https://openalex.org/W2149498546","https://openalex.org/W2152657433","https://openalex.org/W2167433878","https://openalex.org/W2292609460","https://openalex.org/W2303654018","https://openalex.org/W2323824333","https://openalex.org/W2606152085","https://openalex.org/W2897923806","https://openalex.org/W2900571136","https://openalex.org/W2949400887","https://openalex.org/W2950596115","https://openalex.org/W2962885101","https://openalex.org/W2963172141","https://openalex.org/W2963433607","https://openalex.org/W2963549562","https://openalex.org/W2963550745","https://openalex.org/W2963752762","https://openalex.org/W2963874162","https://openalex.org/W2963956018","https://openalex.org/W2964015695","https://openalex.org/W2970252576","https://openalex.org/W2992915149","https://openalex.org/W3005085468","https://openalex.org/W3042776612","https://openalex.org/W3098700198","https://openalex.org/W3101653947"],"related_works":["https://openalex.org/W110881745","https://openalex.org/W3105666697","https://openalex.org/W1998541716","https://openalex.org/W3091150819","https://openalex.org/W3165111336","https://openalex.org/W2905328479","https://openalex.org/W3193097237","https://openalex.org/W3166559945","https://openalex.org/W2891263010","https://openalex.org/W107279588","https://openalex.org/W3176592938","https://openalex.org/W2755410691","https://openalex.org/W2078639717","https://openalex.org/W1606094025","https://openalex.org/W3177956798","https://openalex.org/W2987732919","https://openalex.org/W2780035115","https://openalex.org/W3121940467","https://openalex.org/W3128796643","https://openalex.org/W3123458307"],"abstract_inverted_index":{"Bayesian":[0],"inference":[1],"can":[2],"be":[3],"embedded":[4],"into":[5],"an":[6],"appropriately":[7],"defined":[8],"dynamics":[9],"in":[10,83],"the":[11,56,61,84,87,125,128],"space":[12],"of":[13,58,86,112,127],"probability":[14],"measures.":[15],"In":[16,65],"this":[17],"paper,":[18],"we":[19,43,108],"take":[20],"Brownian":[21],"motion":[22],"and":[23,35,47,52],"its":[24],"associated":[25],"Fokker--Planck":[26],"equation":[27],"as":[28],"a":[29],"starting":[30],"point":[31],"for":[32,80,101],"such":[33,92],"embeddings":[34],"explore":[36],"several":[37],"interacting":[38,49],"particle":[39,50],"approximations.":[40,121],"More":[41],"specifically,":[42],"consider":[44],"both":[45],"deterministic":[46],"stochastic":[48],"systems":[51],"combine":[53],"them":[54],"with":[55],"idea":[57],"preconditioning":[59,78],"by":[60,117],"empirical":[62],"covariance":[63],"matrix.":[64],"addition":[66],"to":[67,69,98,114],"leading":[68],"affine":[70],"invariant":[71],"formulations":[72],"which":[73],"asymptotically":[74],"speed":[75],"up":[76],"convergence,":[77],"allows":[79],"gradient-free":[81,93,120],"implementations":[82,94],"spirit":[85],"ensemble":[88],"Kalman":[89],"filter.":[90],"While":[91],"have":[95],"been":[96],"demonstrated":[97],"work":[99],"well":[100],"posterior":[102],"measures":[103,116],"that":[104],"are":[105],"nearly":[106],"Gaussian,":[107],"extend":[109],"their":[110],"scope":[111],"applicability":[113],"multimodal":[115],"introducing":[118],"localised":[119],"Numerical":[122],"results":[123],"demonstrate":[124],"effectiveness":[126],"considered":[129],"methodologies.":[130]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
