{"id":"W2730088321","doi":"10.48550/arxiv.1605.06359","title":"Learning to Discover Sparse Graphical Models","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"KU Leuven; Agence Nationale de la Recherche","keywords":"Computer science; Inference; Leverage (statistics); Graphical model; Prior probability; Machine learning; Synthetic data; Artificial intelligence; Graph; Data mining; Theoretical computer science; Algorithm; Bayesian probability","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":[0.004623133,0.001259986,0.001788191,0.002876758,0.0007083683,0.001731307,0.003109388,0.002914232,0.001873855],"category_scores_gemma":[0.0334005,0.001149134,0.001649928,0.003066697,0.002830423,0.003741531,0.002290622,0.003343764,0.0005627032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00148363,"about_ca_system_score_gemma":0.001154298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004554365,"about_ca_topic_score_gemma":0.004876991,"domain_scores_codex":[0.997376,0.001350944,0.00008480843,0.0007253244,0.0003511034,0.0001119108],"domain_scores_gemma":[0.9726611,0.023435,0.00147947,0.001510357,0.0005942325,0.0003197876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008993007,0.0001380548,0.004814382,0.0004312379,0.0002822266,0.0002920279,0.000280355,0.7454237,0.001220184,0.152987,0.003527585,0.09051334],"study_design_scores_gemma":[0.00001487868,0.00001867968,0.0003121585,0.00002034699,0.00001613666,0.00004120354,0.00001527662,0.7553916,0.0002922241,0.2429467,0.0009175386,0.00001326601],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00840486,0.0003026787,0.9895191,0.0008262388,0.00001769024,0.000027924,0.0001993514,0.0002556079,0.0004465991],"genre_scores_gemma":[0.3871442,0.00182038,0.6045351,0.0008515205,0.0003755584,0.0003080264,0.001978656,0.0001919662,0.002794572],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004623133,"threshold_uncertainty_score":0.02444977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09450498056568445,"score_gpt":0.2008392823280467,"score_spread":0.1063343017623622,"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."}}