{"id":"W2000045933","doi":"10.1214/11-ejs594","title":"A Metropolis-Hastings based method for sampling from the G-Wishart distribution in Gaussian graphical models","year":2011,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto; Toronto Public Health","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Wishart distribution; Mathematics; Metropolis–Hastings algorithm; Gaussian; Deviance (statistics); Graphical model; Conjugate prior; Sampling (signal processing); Statistics; Applied mathematics; Algorithm; Prior probability; Computer science; Markov chain Monte Carlo; Bayesian probability; Multivariate statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.002448103,0.0001953003,0.0004727578,0.00008266793,0.0001130034,0.00004482795,0.0003491559,0.00009973217,0.00009172881],"category_scores_gemma":[0.004963204,0.0001344688,0.0001244232,0.0002664915,0.00009754261,0.0001068113,0.0000248396,0.0007243062,7.151997e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002160661,"about_ca_system_score_gemma":0.0003951081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001728735,"about_ca_topic_score_gemma":0.0001839907,"domain_scores_codex":[0.9976456,0.0003475393,0.0008595978,0.000191251,0.000340608,0.0006153882],"domain_scores_gemma":[0.9906487,0.008179752,0.0005539713,0.0001934751,0.000311478,0.000112596],"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":[0.0002046914,0.0001260975,0.0001378656,0.00002602472,0.00006132044,0.000007526323,0.0002768264,0.00001199824,0.00005689225,0.9832876,0.0006764273,0.01512674],"study_design_scores_gemma":[0.000820937,0.0004182704,0.0007850472,0.0001037564,0.0001800023,0.00001751416,0.0001614647,0.0546963,0.0002329954,0.9422739,0.0001612434,0.0001486161],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007624174,0.0001494732,0.9973553,0.0002315838,0.0001048766,0.0002198513,0.001108594,0.00001019089,0.00005767473],"genre_scores_gemma":[0.1498899,0.00003021369,0.8498026,0.0001005619,0.0001098744,0.00001241981,0.00002773342,0.00002383915,0.000002771821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1491275,"threshold_uncertainty_score":0.5941777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101723487055519,"score_gpt":0.3771730557496278,"score_spread":0.2754495686941087,"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."}}