{"id":"https://openalex.org/W7120079413","doi":"https://doi.org/10.48550/arxiv.2601.04396","title":"Inference in the presence of model-form uncertainties: Leveraging a prediction-oriented approach to improve uncertainty characterization","display_name":"Inference in the presence of model-form uncertainties: Leveraging a prediction-oriented approach to improve uncertainty characterization","publication_year":2026,"publication_date":"2026-01-07","ids":{"openalex":"https://openalex.org/W7120079413","doi":"https://doi.org/10.48550/arxiv.2601.04396"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.04396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.04396","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.2601.04396","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122617893","display_name":"Rebekah White","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"White, Rebekah","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089315313","display_name":"Rileigh Bandy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bandy, Rileigh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Portone, Teresa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Portone, Teresa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.24009999632835388,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.24009999632835388,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.1859000027179718,"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":0.1485999971628189,"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/inference","display_name":"Inference","score":0.8009999990463257},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.6345000267028809},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.60589998960495},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5756999850273132},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5626999735832214},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5385000109672546},{"id":"https://openalex.org/keywords/predictive-inference","display_name":"Predictive inference","score":0.5131000280380249},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5052000284194946},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44670000672340393},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.4239000082015991}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.8009999990463257},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6611999869346619},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.6345000267028809},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.60589998960495},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5756999850273132},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5626999735832214},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5385000109672546},{"id":"https://openalex.org/C917703","wikidata":"https://www.wikidata.org/wiki/Q7239668","display_name":"Predictive inference","level":5,"score":0.5131000280380249},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5052000284194946},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49050000309944153},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44670000672340393},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.4239000082015991},{"id":"https://openalex.org/C95167961","wikidata":"https://www.wikidata.org/wiki/Q4483495","display_name":"Fiducial inference","level":5,"score":0.4043999910354614},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4041000008583069},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4018000066280365},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.40049999952316284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3799999952316284},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.37860000133514404},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.35019999742507935},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.3368000090122223},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3337000012397766},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C176147448","wikidata":"https://www.wikidata.org/wiki/Q1889114","display_name":"Sensitivity analysis","level":3,"score":0.32580000162124634},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.32420000433921814},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.3190999925136566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3156999945640564},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.28349998593330383},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.28290000557899475},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C123614077","wikidata":"https://www.wikidata.org/wiki/Q1364905","display_name":"Propagation of uncertainty","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2563999891281128},{"id":"https://openalex.org/C2780841128","wikidata":"https://www.wikidata.org/wiki/Q5073781","display_name":"Characterization (materials science)","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.04396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.04396","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.2601.04396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.04396","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":[{"display_name":"Peace, Justice and strong institutions","score":0.7878117561340332,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Bayesian":[0],"inference":[1,37,97,167,210],"is":[2,18,122,164],"a":[3,107,111],"popular":[4],"approach":[5],"to":[6,23,46,57,168,181,187,208,215],"calibrating":[7],"uncertainties,":[8],"but":[9],"it":[10],"can":[11,201],"underpredict":[12],"such":[13],"uncertainties":[14,43],"when":[15],"model":[16,109,192],"misspecification":[17,50,138],"present,":[19],"impacting":[20],"its":[21],"reliability":[22],"inform":[24],"decision":[25],"making.":[26],"Recently,":[27],"the":[28,47,61,125,129,134,146,169,182,216],"statistics":[29],"and":[30,44,86,110,139,141,153],"machine":[31],"learning":[32],"communities":[33],"have":[34,55],"developed":[35],"prediction-oriented":[36,96,120,166,199],"approaches":[38,54],"that":[39,185,198],"provide":[40,202],"better":[41,203],"calibrated":[42],"adapt":[45],"level":[48],"of":[49,63,69,82,128,136,145,161,171,218],"present.":[51],"However,":[52],"these":[53,101],"yet":[56],"be":[58],"demonstrated":[59],"in":[60,98,206],"context":[62],"complex":[64],"scientific":[65],"applications":[66],"where":[67],"phenomena":[68],"interest":[70],"are":[71,177],"governed":[72,115],"by":[73,116],"physics-based":[74,179],"models.":[75],"Such":[76],"settings":[77],"often":[78],"involve":[79],"single":[80],"realizations":[81],"high-dimensional":[83],"spatio-temporal":[84],"data":[85],"nonlinear,":[87],"computationally":[88],"expensive":[89],"parameter-to-observable":[90],"maps.":[91],"This":[92],"work":[93,163],"investigates":[94],"variational":[95],"problems":[99],"exhibiting":[100],"relevant":[102],"features;":[103],"namely,":[104],"we":[105,142],"consider":[106],"polynomial":[108],"contaminant":[112],"transport":[113],"problem":[114],"advection-diffusion":[117],"equations.":[118],"The":[119,194],"loss":[121],"formulated":[123],"as":[124],"log-predictive":[126],"probability":[127],"calibration":[130,170,217],"data.":[131],"We":[132],"study":[133],"effects":[135],"increasing":[137],"noise,":[140],"assess":[143],"approximations":[144],"predictive":[147],"density":[148,156],"using":[149],"Monte":[150],"Carlo":[151],"sampling":[152],"component-wise":[154],"kernel":[155],"estimation.":[157],"A":[158],"novel":[159],"aspect":[160],"this":[162],"applying":[165],"model-form":[172],"uncertainty":[173,204],"(MFU)":[174],"representations,":[175],"which":[176],"embedded":[178],"modifications":[180],"governing":[183],"equations":[184],"aim":[186],"reduce":[188],"(but":[189],"rarely":[190],"eliminate)":[191],"misspecification.":[193],"computational":[195],"results":[196],"demonstrate":[197],"frameworks":[200],"characterizations":[205],"comparison":[207],"standard":[209],"while":[211],"also":[212],"being":[213],"amenable":[214],"MFU":[219],"representations.":[220]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-10T00:00:00"}