{"id":"https://openalex.org/W7131438338","doi":"https://doi.org/10.48550/arxiv.2602.19597","title":"Neural Markov chain Monte Carlo: Bayesian inversion via normalizing flows and variational autoencoders","display_name":"Neural Markov chain Monte Carlo: Bayesian inversion via normalizing flows and variational autoencoders","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7131438338","doi":"https://doi.org/10.48550/arxiv.2602.19597"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.19597","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19597","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":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.2602.19597","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126718382","display_name":"Giacomo Bottacini","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bottacini, Giacomo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021009505","display_name":"Matteo Torzoni","orcid":"https://orcid.org/0000-0003-0027-9788"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Torzoni, Matteo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026300299","display_name":"Andrea Manzoni","orcid":"https://orcid.org/0000-0002-7911-8575"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manzoni, Andrea","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5126718382"],"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.3743000030517578,"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"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.3743000030517578,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.10170000046491623,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.09669999778270721,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"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":0.6225000023841858},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4205999970436096},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.39649999141693115},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.3887999951839447},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.3785000145435333},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.36800000071525574},{"id":"https://openalex.org/keywords/metropolis\u2013hastings-algorithm","display_name":"Metropolis\u2013Hastings algorithm","score":0.364300012588501},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3555000126361847},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.33899998664855957},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.3257000148296356}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6225000023841858},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5285999774932861},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46320000290870667},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4205999970436096},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.3785000145435333},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37049999833106995},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.36800000071525574},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.364300012588501},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3555000126361847},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35190001130104065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34549999237060547},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.3125},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.30000001192092896},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2906000018119812},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.2676999866962433},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2547999918460846},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.19597","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19597","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.19597","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19597","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Clean water and sanitation","score":0.5429190993309021,"id":"https://metadata.un.org/sdg/6"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,62,121,175,192],"Bayesian":[4],"framework":[5],"that":[6,25,209],"combines":[7],"Markov":[8],"chain":[9],"Monte":[10],"Carlo":[11],"(MCMC)":[12],"sampling,":[13,56],"dimensionality":[14],"reduction,":[15],"and":[16,32,86,108,136,182,184],"neural":[17,122,147],"density":[18],"estimation":[19,186],"to":[20,113],"efficiently":[21],"handle":[22],"inverse":[23],"problems":[24],"(i)":[26,170],"must":[27],"be":[28,114],"solved":[29],"multiple":[30],"times,":[31],"(ii)":[33,185],"are":[34,118,144],"characterized":[35],"by":[36,58,146],"intractable":[37],"or":[38],"unavailable":[39],"likelihood":[40,123],"functions.":[41],"The":[42,72,134,161,198],"posterior":[43],"probability":[44],"distribution":[45,107],"over":[46],"quantities":[47],"of":[48,94,111,139,157,174,187,203],"interest":[49],"is":[50,164],"estimated":[51],"via":[52],"differential":[53],"evolution":[54],"Metropolis":[55],"empowered":[57],"learnable":[59],"mappings.":[60],"First,":[61],"variational":[63],"autoencoder":[64],"performs":[65],"probabilistic":[66],"feature":[67],"extraction":[68],"from":[69,103],"observational":[70],"data.":[71],"resulting":[73],"latent":[74,106],"structure":[75],"inherently":[76],"quantifies":[77],"uncertainty,":[78],"capturing":[79],"deviations":[80],"between":[81],"the":[82,87,95,99,104,109,140,151,155,188,201,204],"actual":[83],"data-generating":[84],"process":[85],"training":[88],"data":[89],"distribution.":[90],"At":[91],"each":[92],"step":[93],"MCMC":[96],"random":[97],"walk,":[98],"algorithm":[100],"jointly":[101],"samples":[102,117],"data-informed":[105],"space":[110],"parameters":[112],"inferred.":[115],"These":[116],"fed":[119],"into":[120],"estimator":[124],"based":[125],"on":[126,150,166],"normalizing":[127],"flows,":[128],"specifically":[129],"real-valued":[130],"non-volume":[131],"preserving":[132],"transformations.":[133],"scaling":[135],"translation":[137],"functions":[138],"affine":[141],"coupling":[142],"layers":[143],"modeled":[145],"networks":[148],"conditioned":[149],"unknown":[152],"parameters,":[153],"allowing":[154],"representation":[156],"arbitrary":[158],"observation":[159],"likelihoods.":[160],"proposed":[162],"methodology":[163],"validated":[165],"two":[167],"case":[168],"studies:":[169],"structural":[171],"health":[172],"monitoring":[173],"railway":[176],"bridge":[177],"for":[178],"damage":[179],"detection,":[180],"localization,":[181],"quantification,":[183],"conductivity":[189],"field":[190],"in":[191],"steady-state":[193],"Darcy's":[194],"groundwater":[195],"flow":[196],"problem.":[197],"results":[199],"demonstrate":[200],"efficiency":[202],"inference":[205],"strategy,":[206],"while":[207],"ensuring":[208],"model-reality":[210],"mismatches":[211],"do":[212],"not":[213],"yield":[214],"overconfident,":[215],"yet":[216],"inaccurate,":[217],"estimates.":[218]},"counts_by_year":[],"updated_date":"2026-02-26T06:34:08.959763","created_date":"2026-02-26T00:00:00"}
