{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003589589,0.0004849836,0.0007820782,0.0001687746,0.0001378631,0.00008356023,0.0006868418,0.0004358889,0.00006925708],"category_scores_gemma":[0.0007421793,0.0004357604,0.0002427291,0.0002424959,0.0001836764,0.0001117882,0.0005127203,0.0007128393,0.00001235073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001102103,"about_ca_system_score_gemma":0.0002394812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001249871,"about_ca_topic_score_gemma":0.00005618789,"domain_scores_codex":[0.9977949,0.0002143771,0.0003258886,0.001039441,0.0001436749,0.0004817112],"domain_scores_gemma":[0.9938844,0.004234833,0.0003323072,0.0009503559,0.00037923,0.0002188382],"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":[0.0002752493,0.00008806893,0.0003290364,0.0004872621,0.0001082807,0.00009781219,0.0001242753,0.2808441,0.00001696626,0.7172542,0.00004854024,0.0003262202],"study_design_scores_gemma":[0.0005874795,0.000104026,0.00002688104,0.0002419394,0.0001755398,0.000002151115,0.00003854171,0.4405957,0.00001436862,0.5578408,0.00001663904,0.0003559061],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3392567,0.00001266563,0.6588677,0.00002063739,0.0001356611,0.0007027428,0.0001547102,0.0001264756,0.0007226974],"genre_scores_gemma":[0.8025256,0.00006192427,0.1966533,0.00006537937,0.00005182144,0.000006474757,0.00002639694,0.00005375288,0.0005553607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4632689,"threshold_uncertainty_score":0.9998094,"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."}}