{"id":"https://openalex.org/W7123339012","doi":"https://doi.org/10.1109/cdc57313.2025.11312924","title":"A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior","display_name":"A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior","publication_year":2025,"publication_date":"2025-12-09","ids":{"openalex":"https://openalex.org/W7123339012","doi":"https://doi.org/10.1109/cdc57313.2025.11312924"},"language":null,"primary_location":{"id":"doi:10.1109/cdc57313.2025.11312924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc57313.2025.11312924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 64th Conference on Decision and Control (CDC)","raw_type":"proceedings-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/A5101354256","display_name":"Yibo Shi","orcid":"https://orcid.org/0000-0003-1523-6801"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Yibo Shi","raw_affiliation_strings":["KTH Royal Institute of Technology,Division of Decision and Control Systems,Stockholm,Sweden,SE-100 44"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,Division of Decision and Control Systems,Stockholm,Sweden,SE-100 44","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019461478","display_name":"Braghadeesh Lakshminarayanan","orcid":"https://orcid.org/0009-0008-4893-0473"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Braghadeesh Lakshminarayanan","raw_affiliation_strings":["KTH Royal Institute of Technology,Division of Decision and Control Systems,Stockholm,Sweden,SE-100 44"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,Division of Decision and Control Systems,Stockholm,Sweden,SE-100 44","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057105677","display_name":"Cristian R. Rojas","orcid":"https://orcid.org/0000-0003-0355-2663"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Cristian R. Rojas","raw_affiliation_strings":["KTH Royal Institute of Technology,Division of Decision and Control Systems,Stockholm,Sweden,SE-100 44"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,Division of Decision and Control Systems,Stockholm,Sweden,SE-100 44","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101354256"],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.67252519,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2359","last_page":"2364"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.7820000052452087,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.7820000052452087,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.04170000180602074,"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"}},{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.03669999912381172,"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/prior-probability","display_name":"Prior probability","score":0.8342000246047974},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6015999913215637},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.48890000581741333},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4674000144004822},{"id":"https://openalex.org/keywords/fisher-information","display_name":"Fisher information","score":0.439300000667572},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.438400000333786},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.39899998903274536},{"id":"https://openalex.org/keywords/parameter-space","display_name":"Parameter space","score":0.391400009393692},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.37790000438690186}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.8342000246047974},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6015999913215637},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5927000045776367},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.536899983882904},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.48890000581741333},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4674000144004822},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4410000145435333},{"id":"https://openalex.org/C29406490","wikidata":"https://www.wikidata.org/wiki/Q1420659","display_name":"Fisher information","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.438400000333786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4004000127315521},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.391400009393692},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.37790000438690186},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.3700999915599823},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.3424000144004822},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.3301999866962433},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.30219998955726624},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2980000078678131},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.2867000102996826},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.2822999954223633},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cdc57313.2025.11312924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc57313.2025.11312924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 64th Conference on Decision and Control (CDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Inference":[0],"and":[1,9,154,172],"estimation":[2],"are":[3],"fundamental":[4],"in":[5,93,149],"statistics,":[6],"system":[7,17,151],"identification,":[8],"machine":[10],"learning.":[11],"When":[12],"prior":[13,31,45,59,64,108],"knowledge":[14,36],"about":[15],"the":[16,48,74,79,84,88,96,121,134,170,175],"is":[18,37,60,68,109],"available,":[19],"Bayesian":[20,155],"analysis":[21],"provides":[22],"a":[23,30,43,116],"natural":[24],"framework":[25],"for":[26],"encoding":[27],"it":[28,67,77],"through":[29,87],"distribution.":[32],"In":[33,111],"practice,":[34],"such":[35],"often":[38],"too":[39],"vague":[40],"to":[41,138],"specify":[42],"full":[44],"distribution,":[46],"motivating":[47],"use":[49],"of":[50,73,83,98,128,163,174],"default":[51],"\"uninformative\"":[52],"priors":[53],"that":[54,125],"minimize":[55],"subjective":[56],"bias.":[57],"Jeffreys":[58,107,132],"an":[61,159],"appealing":[62],"uninformative":[63],"because:":[65],"(i)":[66],"invariant":[69],"under":[70],"any":[71],"re-parameterization":[72],"model,":[75],"(ii)":[76],"encodes":[78],"intrinsic":[80],"geometric":[81],"structure":[82],"parameter":[85,99,129,164],"space":[86],"Fisher":[89],"information":[90],"matrix,":[91],"which":[92],"turn":[94],"enhances":[95],"diversity":[97],"samples.":[100],"Despite":[101],"these":[102],"benefits,":[103],"drawing":[104],"samples":[105,144],"from":[106,131],"challenging.":[110],"this":[112],"paper,":[113],"we":[114],"develop":[115],"general":[117],"sampling":[118,127],"scheme":[119],"using":[120],"Metropolis-Adjusted":[122],"Langevin":[123],"Algorithm":[124],"enables":[126],"values":[130],"prior;":[133],"method":[135],"extends":[136],"naturally":[137],"nonlinear":[139],"state\u2013space":[140],"models.":[141],"The":[142],"resulting":[143],"can":[145],"be":[146],"directly":[147],"used":[148],"sampling-based":[150],"identification":[152],"methods":[153],"experiment":[156],"design,":[157],"providing":[158],"objective,":[160],"information-geometric":[161],"description":[162],"uncertainty.":[165],"Several":[166],"numerical":[167],"examples":[168],"demonstrate":[169],"efficiency":[171],"accuracy":[173],"proposed":[176],"scheme.":[177]},"counts_by_year":[],"updated_date":"2026-01-14T00:46:21.520733","created_date":"2026-01-14T00:00:00"}