{"id":"W3182878663","doi":"10.1093/imaiai/iaaf019","title":"Performance of Bayesian linear regression in a model with mismatch","year":2025,"lang":"en","type":"article","venue":"Information and Inference A Journal of the IMA","topic":"Control Systems and Identification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Bayesian linear regression; Bayesian multivariate linear regression; Bayesian probability; Linear regression; Proper linear model; Regression; Statistics; Linear model; Regression analysis; Econometrics; General linear model; Computer science; Mathematics; Bayesian inference","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001536527,0.00003892752,0.00008876596,0.0001103241,0.00002040378,0.00001890327,0.00007619801,0.00002236891,0.000001686344],"category_scores_gemma":[0.00002288771,0.00002197313,0.00001593056,0.0001206097,0.00001208019,0.000675877,0.000009032358,0.00008227006,4.342753e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001788776,"about_ca_system_score_gemma":0.00004129579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007663307,"about_ca_topic_score_gemma":0.000007422649,"domain_scores_codex":[0.9995257,0.000006739658,0.0003281625,0.00001320767,0.0000852375,0.00004094538],"domain_scores_gemma":[0.9996728,0.00001063292,0.0001397868,0.00006536645,0.00009982946,0.00001157191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002600062,0.00002982373,0.06527451,0.0008871458,0.00005419227,3.312666e-7,0.005675185,0.794784,0.00780409,0.001234319,0.0009359789,0.1230604],"study_design_scores_gemma":[0.0003907973,0.00001821617,0.02169461,0.0006076964,0.00000501606,0.000005115936,0.0001180688,0.9753881,0.001403844,0.00006446052,0.0002770018,0.00002703502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797853,0.00007764145,0.01863917,0.0001863009,0.00007180822,0.00005986413,8.670742e-7,0.00000381654,0.001175266],"genre_scores_gemma":[0.9995449,0.000113002,0.0002774156,0.00002127677,0.000004726559,0.000001218995,2.72922e-7,0.000001090557,0.00003610313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1806041,"threshold_uncertainty_score":0.08960381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005053991288399349,"score_gpt":0.2140959495085547,"score_spread":0.2090419582201553,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}