{"id":"https://openalex.org/W7162085158","doi":"https://doi.org/10.48550/arxiv.2605.21717","title":"Likelihood-informed dimension reduction across tempered Bayesian posteriors","display_name":"Likelihood-informed dimension reduction across tempered Bayesian posteriors","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7162085158","doi":"https://doi.org/10.48550/arxiv.2605.21717"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21717","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21717","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.21717","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033837407","display_name":"Arne Bouillon","orcid":"https://orcid.org/0000-0002-2745-1982"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bouillon, Arne","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5014826295","display_name":"Oliver R. A. Dunbar","orcid":"https://orcid.org/0000-0001-7374-0382"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dunbar, Oliver R. A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.7426999807357788,"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":0.7426999807357788,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.06530000269412994,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.062300000339746475,"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/emulation","display_name":"Emulation","score":0.6297000050544739},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5551999807357788},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.542900025844574},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5206000208854675},{"id":"https://openalex.org/keywords/effective-dimension","display_name":"Effective dimension","score":0.49160000681877136},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.48030000925064087},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.41530001163482666},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.35109999775886536},{"id":"https://openalex.org/keywords/uncertainty-reduction-theory","display_name":"Uncertainty reduction theory","score":0.33410000801086426}],"concepts":[{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.6297000050544739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6226000189781189},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5551999807357788},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.542900025844574},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5206000208854675},{"id":"https://openalex.org/C115311070","wikidata":"https://www.wikidata.org/wiki/Q5347255","display_name":"Effective dimension","level":3,"score":0.49160000681877136},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.48030000925064087},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46889999508857727},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3977999985218048},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38269999623298645},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C94361409","wikidata":"https://www.wikidata.org/wiki/Q7882500","display_name":"Uncertainty reduction theory","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.33009999990463257},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3246000111103058},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.3138999938964844},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.2831000089645386},{"id":"https://openalex.org/C153914771","wikidata":"https://www.wikidata.org/wiki/Q5227343","display_name":"Data reduction","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.2793999910354614},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27070000767707825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2702000141143799},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.258899986743927},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25450000166893005},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21717","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21717","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.21717","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21717","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"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":{"Scientific":[0],"computer":[1],"simulations":[2],"cannot":[3],"represent":[4],"all":[5],"scales":[6],"in":[7,46,102,123],"realistic":[8],"applications.":[9],"To":[10,28],"bridge":[11],"this":[12,66,105,110],"model-data":[13],"gap,":[14],"parameters":[15],"are":[16,99,209],"injected":[17],"into":[18],"models":[19],"and":[20,64,92,162,166,183],"constrained":[21],"with":[22,48],"noisy":[23],"data":[24,158,171],"using":[25],"Bayesian":[26],"inversion.":[27],"reduce":[29],"the":[30,51,56,156,173,230],"number":[31],"of":[32,50,61,176,187,199],"simulator":[33],"evaluations,":[34],"which":[35],"can":[36,140,194],"be":[37,195],"10^5":[38],"or":[39,204,211],"more,":[40],"modern":[41],"approaches":[42],"employ":[43],"dimension":[44,67],"reduction":[45,68],"conjunction":[47],"emulation":[49,198],"forward":[52,200],"map":[53],"(that":[54],"contains":[55],"simulator).":[57],"Due":[58],"to":[59,83,96,112,115,128,152,214,225],"scarcity":[60],"model":[62,188],"evaluations":[63],"data,":[65],"becomes":[69],"very":[70],"important":[71],"for":[72,121,169,197,202],"posterior":[73],"sampling":[74],"performance.":[75],"Recent":[76],"work":[77],"on":[78,89,149],"likelihood-informed":[79],"subspaces":[80],"(LIS)":[81],"truncates":[82],"informative":[84],"directions":[85],"by":[86],"optimizing":[87],"bounds":[88],"information":[90],"loss,":[91],"though":[93],"mathematically":[94],"well-adapted":[95],"sampling,":[97],"they":[98],"often":[100,141],"restrictive":[101],"practice.":[103],"In":[104,145,216],"work,":[106],"we":[107,147],"provably":[108],"generalize":[109],"methodology":[111],"facilitate":[113],"application":[114],"$\u03b1$-tempered":[116],"(i.e.,":[117],"annealed,":[118],"power-posterior)":[119],"distributions":[120,177],"$\u03b1$":[122,137,233],"[0,1].":[124],"We":[125,134,164],"provide":[126],"theory":[127],"build":[129],"partially-informed":[130],"spaces":[131],"termed":[132],"$\u03b1$-LIS.":[133],"show":[135],"how":[136],"&lt;":[138,179,181],"1":[139],"produce":[142],"near-optimal":[143],"spaces.":[144],"addition,":[146],"focus":[148],"applying":[150],"$\u03b1$-LIS":[151],"practical":[153],"cases,":[154],"where":[155,207],"available":[157],"is":[159,221],"severely":[160],"limited":[161],"noisy.":[163],"propose":[165],"test":[167],"extensions":[168],"utilizing":[170],"from":[172],"entire":[174],"sequence":[175],"$\u03b1$_0":[178],"...":[180],"$\u03b1$_k,":[182],"use":[184],"simple":[185],"approximations":[186],"gradients":[189],"so":[190],"that":[191],"our":[192,218],"approach":[193,220],"used":[196],"maps":[201],"chaotic":[203],"stochastic":[205],"systems":[206],"derivatives":[208],"unavailable":[210],"uninformative":[212],"due":[213],"noise.":[215],"experiments,":[217],"accumulated":[219],"much":[222],"more":[223],"robust":[224],"these":[226],"challenging":[227],"circumstances":[228],"than":[229],"theoretically":[231],"optimal":[232],"=":[234],"1.":[235]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-23T00:00:00"}
