{"id":"W2890740717","doi":"10.1109/tsmc.2018.2864752","title":"A Novel Semi-Supervised Sparse Bayesian Regression Based on Variational Inference for Industrial Datasets With Incomplete Outputs","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Prior probability; Missing data; Inference; Computer science; Posterior probability; Bayesian inference; Bayesian probability; Artificial intelligence; Bayesian linear regression; Regression; Machine learning; Pattern recognition (psychology); Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003794326,0.0004492267,0.0005353045,0.0003255927,0.000325737,0.0002936396,0.0002045684,0.0003218372,0.00001520662],"category_scores_gemma":[0.00001041038,0.0003714283,0.00008471801,0.0002874522,0.00008337093,0.0001326385,0.000001786949,0.0003043743,0.00003600875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001557907,"about_ca_system_score_gemma":0.00007159169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003884589,"about_ca_topic_score_gemma":0.0001872967,"domain_scores_codex":[0.9977531,0.0001345982,0.0006532714,0.0005109763,0.0005406942,0.000407344],"domain_scores_gemma":[0.9986001,0.0002817404,0.0001568888,0.0005512375,0.0001516108,0.0002584011],"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":[0.001883533,0.0005518622,0.0001507487,0.001381563,0.000679993,0.00001647206,0.001045112,0.9677586,0.01383467,0.001658808,0.004939294,0.006099342],"study_design_scores_gemma":[0.004399244,0.0008704896,0.00004557829,0.001134087,0.00007833871,0.00005092544,0.0002798524,0.9777486,0.0006885948,0.000002738645,0.01422908,0.0004724271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008448329,0.00006780357,0.9829021,0.00004649381,0.003924813,0.001856589,0.001566023,0.000356181,0.0008316335],"genre_scores_gemma":[0.9979431,0.00000562063,0.0001981374,0.0000513921,0.0006658468,0.000569542,0.0000791187,0.00008257053,0.0004046627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9894948,"threshold_uncertainty_score":0.9998738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02968592826681079,"score_gpt":0.238735310874566,"score_spread":0.2090493826077552,"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."}}