{"id":"W1983690667","doi":"10.1016/s0004-3702(02)00191-1","title":"Learning Bayesian networks from data: An information-theory based approach","year":2002,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":803,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Carnegie Mellon University; University of Washington","keywords":"Bayesian network; Computer science; Conditional independence; Machine learning; Artificial intelligence; Bayesian probability; Independence (probability theory); Variable-order Bayesian network; Data mining; Bayesian inference; Theoretical computer science; Mathematics","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":[0.01474687,0.002095267,0.004896785,0.008417371,0.001536681,0.007586886,0.005870212,0.005004172,0.003737747],"category_scores_gemma":[0.06591768,0.00240468,0.003781907,0.007695709,0.005287538,0.01364977,0.004280712,0.0065506,0.000826766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003181224,"about_ca_system_score_gemma":0.002878453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005794742,"about_ca_topic_score_gemma":0.005766318,"domain_scores_codex":[0.9886805,0.00642094,0.0007592286,0.001470166,0.002418604,0.0002505772],"domain_scores_gemma":[0.9297633,0.06346994,0.002346915,0.002163725,0.001741557,0.0005145081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001300456,0.0002035618,0.002264219,0.0009938976,0.0008214922,0.0003089376,0.0004347991,0.2641813,0.0004613588,0.5802218,0.00315763,0.146821],"study_design_scores_gemma":[0.00002805369,0.00002237412,0.0002270754,0.0001004848,0.0001107824,0.0000922215,0.00003214317,0.2460774,0.0001593006,0.7514909,0.001623126,0.00003618779],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001384827,0.00144992,0.9952276,0.0008319672,0.00002825194,0.00004226849,0.0001000675,0.00005886357,0.0008762847],"genre_scores_gemma":[0.2164024,0.009205629,0.7692318,0.0008329971,0.0009466074,0.0005895676,0.0008323761,0.0001068719,0.001851828],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01474687,"threshold_uncertainty_score":0.07798976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09874819574461036,"score_gpt":0.2791029661627809,"score_spread":0.1803547704181706,"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."}}