{"id":"W2569360723","doi":"10.1002/cjs.11305","title":"Bayesian multiplicity control for multiple graphs","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inference; Graphical model; Computer science; Random graph; Conditional independence; Statistical inference; Bayesian inference; Gibbs sampling; Exponential random graph models; Graph; Algorithm; Bayesian probability; Mathematics; Theoretical computer science; Artificial intelligence; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06446177,0.001833482,0.004129622,0.004100951,0.002325047,0.00502563,0.008202553,0.003940583,0.01212082],"category_scores_gemma":[0.2471021,0.001604994,0.003391693,0.003919762,0.008976161,0.009670253,0.005874947,0.007451539,0.000934914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006481713,"about_ca_system_score_gemma":0.00262133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005478376,"about_ca_topic_score_gemma":0.003211941,"domain_scores_codex":[0.9463763,0.03603107,0.001396625,0.01023589,0.004346868,0.001613213],"domain_scores_gemma":[0.6623155,0.2913753,0.01695757,0.02066867,0.00619567,0.002487323],"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":[0.0003742498,0.0001460901,0.00426639,0.0003344027,0.0005094167,0.0004171062,0.0004088685,0.1458168,0.0008313592,0.785194,0.003299312,0.05840188],"study_design_scores_gemma":[0.00006075662,0.00005543223,0.0008704317,0.00003849225,0.00006125253,0.00005981074,0.00003846825,0.3304763,0.0005260633,0.6663691,0.001407917,0.0000358801],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01078999,0.0005167523,0.9853094,0.001008945,0.00009545319,0.0001119359,0.0002316598,0.0003658226,0.001570033],"genre_scores_gemma":[0.6325039,0.0006656761,0.3578431,0.0008489062,0.0006802373,0.001030134,0.001029773,0.0004531547,0.004945137],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06446177,"threshold_uncertainty_score":0.3409105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0973279761351604,"score_gpt":0.3438506611979792,"score_spread":0.2465226850628188,"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."}}