{"id":"W1999271084","doi":"10.1007/s10260-006-0012-x","title":"Bounds for the Bayes Error in Classification: A Bayesian Approach Using Discriminant Analysis","year":2006,"lang":"en","type":"article","venue":"Statistical Methods & Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Bhattacharyya distance; Linear discriminant analysis; Bayes' theorem; Mathematics; Posterior probability; Bayes error rate; Optimal discriminant analysis; Statistics; Discriminant; Pattern recognition (psychology); Bayesian probability; Naive Bayes classifier; Prior probability; Classification rule; Probability distribution; Artificial intelligence; Dirichlet distribution; Bayes classifier; Computer science","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":[0.002635719,0.0002251189,0.000404513,0.0002757655,0.0004692224,0.0002521309,0.000922596,0.000108714,0.00001205535],"category_scores_gemma":[0.0001847427,0.0001630669,0.0001745547,0.002331995,0.0002294369,0.0001610727,0.0001190708,0.0002139361,0.000001978296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009467694,"about_ca_system_score_gemma":0.0001205907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002666464,"about_ca_topic_score_gemma":0.00008031901,"domain_scores_codex":[0.9971763,0.0007335278,0.0006067356,0.0007985002,0.0002321077,0.0004527693],"domain_scores_gemma":[0.9952524,0.003209494,0.0001724558,0.001129234,0.000125828,0.0001105927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003839127,0.0001182083,0.00008231802,0.00001703873,0.0000435819,3.345428e-7,0.00008753375,0.0002657153,0.0004947774,0.801937,0.000103165,0.1968465],"study_design_scores_gemma":[0.0001057542,0.000009290554,0.006470397,0.000002206488,0.0002387584,0.000003374106,0.00003749563,0.6184928,0.00004669076,0.3713184,0.003128574,0.0001462276],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000005208487,0.0002685739,0.9954641,0.001195027,0.00005711137,0.001115512,0.00009970876,0.00007217161,0.001722622],"genre_scores_gemma":[0.03415262,0.000006299808,0.9634813,0.000127617,0.0001175375,0.00192979,0.00005466915,0.00001912,0.0001110471],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6182271,"threshold_uncertainty_score":0.6649674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07805381755449632,"score_gpt":0.4176665682304711,"score_spread":0.3396127506759748,"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."}}