{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003714885,0.00154994,0.001826114,0.001488703,0.0006220909,0.001467501,0.002030495,0.00178873,0.002367256],"category_scores_gemma":[0.008915018,0.001514763,0.001795382,0.001410456,0.001382215,0.001832447,0.001963811,0.002418378,0.0009170225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001164793,"about_ca_system_score_gemma":0.001946175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006575709,"about_ca_topic_score_gemma":0.004984119,"domain_scores_codex":[0.9978445,0.001001076,0.0000961675,0.000307026,0.0006575863,0.00009375274],"domain_scores_gemma":[0.9973031,0.001848454,0.0002946913,0.0001467191,0.0003550947,0.00005184895],"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":[0.00003894678,0.00003329173,0.0002129199,0.0001154787,0.00008482076,0.00008312617,0.00008894812,0.8841971,0.001254626,0.0745014,0.0006693223,0.03872002],"study_design_scores_gemma":[0.00001188792,0.00002439791,0.00009517052,0.00001599009,0.00001374124,0.00002453218,0.000007821151,0.9640043,0.0003176568,0.03411996,0.001344051,0.00002055823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005478002,0.0002049192,0.9984969,0.00007011835,0.00001080151,0.00001069446,0.00001719355,0.00007797055,0.0005636728],"genre_scores_gemma":[0.246504,0.002300754,0.7411412,0.0002613044,0.0004027783,0.0006519693,0.0004459251,0.0002866676,0.008005357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006575709,"threshold_uncertainty_score":0.01964647,"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."}}