{"id":"W2963656411","doi":"10.1002/cjs.11341","title":"Approximate Bayesian estimation in large coloured graphical Gaussian models","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Graphical model; Gaussian; Rate of convergence; Bayesian probability; Applied mathematics; Matrix (chemical analysis); Bounded function; Statistics; Computer science; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001484203,0.0001907066,0.0005335895,0.0003315874,0.0003847363,0.0003127979,0.0005488347,0.0001460069,0.0002518674],"category_scores_gemma":[0.006088693,0.000170179,0.00007024033,0.0001055149,0.0002377213,0.0002845423,0.00002261765,0.0004833739,0.000003588482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001423136,"about_ca_system_score_gemma":0.0008328076,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009200409,"about_ca_topic_score_gemma":0.02433034,"domain_scores_codex":[0.9979106,0.0001839933,0.0009036118,0.0001716267,0.0003146425,0.0005155784],"domain_scores_gemma":[0.9971167,0.0006126621,0.0007959503,0.0004389686,0.0002942014,0.0007414492],"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":[0.00001818659,0.00003695633,0.003677967,0.00006468043,0.00002276837,0.0007136392,0.0003679705,0.00002437209,0.000004204745,0.9806992,0.002703532,0.01166652],"study_design_scores_gemma":[0.0006188778,0.00009164364,0.01262796,0.00015199,0.00004090758,0.00007766466,0.00007656279,0.1183561,0.00001012913,0.8676817,0.0000982325,0.0001681412],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001647995,0.00002413909,0.9948268,0.0005663605,0.0003218877,0.0001502347,0.0007278405,0.000004840197,0.00172988],"genre_scores_gemma":[0.4638268,0.000006366152,0.5360438,0.00004351919,0.00003515431,0.000002161123,0.000004178544,0.00001636947,0.00002159395],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4621789,"threshold_uncertainty_score":0.9934731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0542792138655318,"score_gpt":0.3402956228710384,"score_spread":0.2860164090055066,"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."}}