{"id":"W2153984633","doi":"10.1002/aic.13887","title":"A Bayesian approach to robust process identification with ARX models","year":2012,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Robustness (evolution); Identification (biology); Hyperparameter; Computer science; Maximum a posteriori estimation; Bayesian probability; Prior probability; Process (computing); Context (archaeology); A priori and a posteriori; Data mining; Machine learning; Artificial intelligence; Mathematics; Statistics; Maximum likelihood","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":[0.0003013646,0.0000956693,0.0001074445,0.00009206178,0.00008417972,0.00009830129,0.0001031645,0.00004728083,0.00001010099],"category_scores_gemma":[0.000006880216,0.00007491242,0.00002985019,0.0001894021,0.000005560463,0.0004561397,0.000003673462,0.0002009567,0.00003592057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006013212,"about_ca_system_score_gemma":0.000011818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003217151,"about_ca_topic_score_gemma":0.000002885088,"domain_scores_codex":[0.9992826,0.00002209704,0.0001915007,0.00007042343,0.0001982386,0.0002351694],"domain_scores_gemma":[0.9995782,0.000005254954,0.0000416517,0.0001119678,0.00005684148,0.000206122],"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":[0.00002081161,0.00004543363,0.0003391754,0.00004530675,0.00006281837,8.230173e-7,0.00203375,0.9917641,0.002154954,0.0001531608,0.001175898,0.002203788],"study_design_scores_gemma":[0.0004385571,0.00002924743,0.001121286,0.00003655972,0.00003129591,0.0005963478,0.001201096,0.9936249,0.001075711,0.00009879172,0.001528008,0.0002181705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06287983,0.0002546948,0.9244036,0.0000756598,0.0003516073,0.0001629453,9.178638e-7,0.0001300261,0.01174072],"genre_scores_gemma":[0.9980969,0.000004761968,0.001087627,0.00004278935,0.000427723,0.00003620206,8.668196e-7,0.00002629318,0.0002768759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.935217,"threshold_uncertainty_score":0.305484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01689239475725788,"score_gpt":0.2149777010020689,"score_spread":0.1980853062448111,"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."}}