{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01348348,0.00114498,0.002144489,0.002216842,0.0007077947,0.0027668,0.003902033,0.002199085,0.002931647],"category_scores_gemma":[0.08271977,0.00134337,0.001564985,0.002498135,0.004364837,0.004713915,0.00358824,0.003851659,0.0006984071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002584095,"about_ca_system_score_gemma":0.001934873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009639,"about_ca_topic_score_gemma":0.007970866,"domain_scores_codex":[0.9945452,0.003208342,0.0001776653,0.0008815606,0.0008933474,0.0002938551],"domain_scores_gemma":[0.9489337,0.04236405,0.002899255,0.003421009,0.001865441,0.0005164931],"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":[0.0001005489,0.00003076651,0.001303952,0.0001435996,0.00009178231,0.0001113309,0.0001669026,0.5950082,0.0008790052,0.3759412,0.001233866,0.02498873],"study_design_scores_gemma":[0.00001374568,0.00001284493,0.0003622234,0.00002523847,0.00001090372,0.00002399898,0.00001287376,0.7714948,0.0002762678,0.2272988,0.0004469005,0.00002141824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008078086,0.0001773341,0.9909385,0.000150817,0.00001426529,0.0000168399,0.00007367639,0.0001304488,0.0004199684],"genre_scores_gemma":[0.5025518,0.001238106,0.4892233,0.0003008436,0.0001905014,0.0003127924,0.000899439,0.0003033871,0.004979779],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01348348,"threshold_uncertainty_score":0.07130831,"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."}}