{"id":"W2949645714","doi":"10.48550/arxiv.1303.1483","title":"Using Causal Information and Local Measures to Learn Bayesian Networks","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bayesian network; Computer science; Minimum description length; Domain (mathematical analysis); Task (project management); Computation; Artificial intelligence; Machine learning; Bayesian probability; Causal structure; Algorithm; Theoretical computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006860301,0.001248731,0.001781317,0.003874625,0.0008573221,0.002150656,0.002904068,0.002113935,0.00358311],"category_scores_gemma":[0.03840324,0.001292468,0.001539686,0.002381675,0.003038333,0.007119905,0.00293699,0.003636918,0.0005507764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002886029,"about_ca_system_score_gemma":0.00136027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002870929,"about_ca_topic_score_gemma":0.004790934,"domain_scores_codex":[0.9964322,0.00189082,0.0001539144,0.0007517666,0.000666899,0.0001044395],"domain_scores_gemma":[0.9710926,0.02423308,0.001622444,0.001668721,0.0009790607,0.0004041194],"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":[0.0001269144,0.00008726403,0.002838372,0.0004170513,0.0002394109,0.0001337947,0.0003419677,0.6094093,0.001634487,0.2869001,0.001014348,0.09685704],"study_design_scores_gemma":[0.00001550502,0.00003663204,0.0002907362,0.00003425555,0.00002545302,0.00002915933,0.00001984871,0.7892596,0.0006253442,0.2087866,0.0008545095,0.00002232121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007243242,0.0003226724,0.9912815,0.0001770973,0.000008042111,0.00002880004,0.00006597251,0.0001605716,0.0007121275],"genre_scores_gemma":[0.3569742,0.001060822,0.6381333,0.0002463812,0.000147889,0.0003523524,0.0007313321,0.0002194962,0.002134248],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006860301,"threshold_uncertainty_score":0.03628117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08927615638073368,"score_gpt":0.2039135353837769,"score_spread":0.1146373790030432,"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."}}