{"id":"W3185747415","doi":"10.1080/01621459.2021.1996377","title":"Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models","year":2021,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Graphical model; Hyperparameter; Bottleneck; Algorithm; Computer science; Gaussian; Bayesian probability; Wishart distribution; Scalability; Graph; Laplace's method; Mathematics; Mathematical optimization; Artificial intelligence; Machine learning; Theoretical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002820258,0.0008564032,0.00143996,0.001192015,0.0006074798,0.001127887,0.002037058,0.00168366,0.003082952],"category_scores_gemma":[0.01616407,0.0008131039,0.00108243,0.001466484,0.0009006076,0.002227685,0.001837413,0.00224802,0.001082789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001486824,"about_ca_system_score_gemma":0.002663458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01271289,"about_ca_topic_score_gemma":0.01963537,"domain_scores_codex":[0.999014,0.000409358,0.00003677733,0.0001527676,0.000295965,0.00009104943],"domain_scores_gemma":[0.9932922,0.005411007,0.00033693,0.0004362797,0.000373167,0.0001505531],"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":[0.00007513751,0.000059334,0.0009907442,0.0000969522,0.0000386314,0.0000454135,0.00009619202,0.8708163,0.001188057,0.03689836,0.002488546,0.08720644],"study_design_scores_gemma":[0.000008610335,0.000005720124,0.00005226704,0.000004648721,0.000003209917,0.000008596394,0.000004012407,0.9867512,0.0002435829,0.01271455,0.0002005576,0.000002941574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01339221,0.000228156,0.9841875,0.000219107,0.00001938006,0.00002776187,0.00006101722,0.001086793,0.0007781257],"genre_scores_gemma":[0.2727435,0.000485886,0.722713,0.0002674921,0.00007059981,0.0001767363,0.0005468228,0.0004357428,0.002560205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01271289,"threshold_uncertainty_score":0.02527779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479983106035805,"score_gpt":0.2752185431962699,"score_spread":0.2604187121359118,"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."}}