{"id":"W2581879608","doi":"10.1007/s10994-016-5619-z","title":"Efficient parameter learning of Bayesian network classifiers","year":2017,"lang":"en","type":"article","venue":"Machine Learning","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Australian Research Council; Asian Office of Aerospace Research and Development","keywords":"Discriminative model; Artificial intelligence; Machine learning; Computer science; Pattern recognition (psychology); Bayesian network; Generative model; Bayesian probability; Generative grammar; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0007951796,0.0001812771,0.0002714144,0.00006894515,0.0009422855,0.0003424555,0.001172472,0.00009315616,0.00002851358],"category_scores_gemma":[0.000500457,0.0001663968,0.0001121885,0.0001240964,0.0001092641,0.0001339652,0.0004695598,0.0006754032,0.00002678577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002169454,"about_ca_system_score_gemma":0.00004372522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001419782,"about_ca_topic_score_gemma":0.000007473984,"domain_scores_codex":[0.9983598,0.0002004825,0.0002912259,0.0004147691,0.0002910925,0.0004426575],"domain_scores_gemma":[0.9985256,0.0001649703,0.000396394,0.0007205906,0.00006969879,0.0001227076],"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":[0.00001231169,0.000033899,0.06130218,0.00002270094,0.00002944164,0.00001651055,0.0006406415,0.8060865,0.0004153427,0.02168654,0.00008081683,0.1096731],"study_design_scores_gemma":[0.0002087989,0.0001149106,0.005686233,0.00006878063,0.000008151313,0.000007126202,0.00001385792,0.9908013,0.0001320059,0.001319818,0.001446377,0.0001926808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05817106,0.0001642226,0.9338402,0.0005169275,0.000296758,0.00006476542,3.141679e-7,0.0001779488,0.006767762],"genre_scores_gemma":[0.9632102,0.00001047643,0.03583412,0.00007132355,0.00009218294,0.000004662114,0.000001839113,0.00001643421,0.0007587433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9050392,"threshold_uncertainty_score":0.7247394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0236781237239863,"score_gpt":0.2688267466209545,"score_spread":0.2451486228969682,"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."}}