{"id":"W1485398918","doi":"10.1007/978-3-540-76928-6_9","title":"Local Learning Algorithm for Markov Blanket Discovery","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Markov blanket; Markov chain; Computer science; Bayesian network; Boundary (topology); Algorithm; Machine learning; Markov model; Scalability; Bayesian probability; Artificial intelligence; Data mining; Mathematics; Variable-order Markov model","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":[0.006380792,0.001054117,0.003767049,0.002525109,0.001642817,0.002388229,0.007721153,0.002928898,0.01315097],"category_scores_gemma":[0.02517766,0.001437918,0.001830289,0.003457956,0.002293429,0.005528294,0.005041272,0.004954505,0.003529801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002031902,"about_ca_system_score_gemma":0.003638921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005064838,"about_ca_topic_score_gemma":0.005141618,"domain_scores_codex":[0.9967821,0.001511967,0.0001609804,0.000651597,0.0006776965,0.0002156916],"domain_scores_gemma":[0.9804255,0.01508617,0.000508389,0.00203542,0.00153191,0.0004125852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007828199,0.0003184557,0.001112524,0.0004463627,0.0002315342,0.0001422782,0.000244416,0.313514,0.001761198,0.2459175,0.01552881,0.4200002],"study_design_scores_gemma":[0.0000601849,0.00003154617,0.00006705913,0.00001708023,0.00002367302,0.00002991246,0.0000123259,0.8998538,0.0005679521,0.09800122,0.001320325,0.00001491771],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001089437,0.0001225983,0.9976074,0.00009304025,0.00001810137,0.00003409003,0.00006100339,0.000492884,0.0004814746],"genre_scores_gemma":[0.06918428,0.0002471376,0.9226724,0.0002240066,0.0001183273,0.0006371263,0.0008574662,0.0004784766,0.005580815],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01315097,"threshold_uncertainty_score":0.04399437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0238752509797168,"score_gpt":0.2672895348689125,"score_spread":0.2434142838891958,"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."}}