{"id":"W2945860003","doi":"10.48550/arxiv.1905.06261","title":"Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"","keywords":"Graphical model; Estimator; Inference; Statistical inference; Frequentist inference; Computer science; Pairwise comparison; Statistical model; Model selection; Gaussian; Algorithm; Mathematics; Predictive inference; Data mining; Artificial intelligence; Bayesian inference; Statistics; 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":[0.01505088,0.001610367,0.002520272,0.003288233,0.0009969111,0.002453581,0.004864817,0.002423159,0.004194909],"category_scores_gemma":[0.08452717,0.001328805,0.002622178,0.003753675,0.002892934,0.005761916,0.005059537,0.004550957,0.0007560026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001500671,"about_ca_system_score_gemma":0.002482239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004416095,"about_ca_topic_score_gemma":0.005182965,"domain_scores_codex":[0.9875579,0.007572114,0.0004878349,0.002477265,0.001412577,0.0004922639],"domain_scores_gemma":[0.9484711,0.04246764,0.002900018,0.003898971,0.001600293,0.0006618847],"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":[0.0001680559,0.000196609,0.007838482,0.0002589835,0.0005268011,0.0003509774,0.0003681614,0.4358941,0.001631296,0.4240539,0.00201778,0.1266949],"study_design_scores_gemma":[0.00002316195,0.0000281654,0.000491136,0.00001659179,0.00003643403,0.00004661963,0.00002359588,0.7551347,0.0003807616,0.2433035,0.0004969728,0.00001829497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006493839,0.00006903488,0.9928414,0.00008454225,0.0000116433,0.00003629316,0.00006805968,0.0001412461,0.000253951],"genre_scores_gemma":[0.4190558,0.0004789563,0.5754532,0.000335635,0.0001486098,0.0005762739,0.001161225,0.0002922904,0.002497877],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01505088,"threshold_uncertainty_score":0.07959765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2053319619175259,"score_gpt":0.279347285406331,"score_spread":0.0740153234888051,"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."}}