{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005253581,0.001029058,0.001227146,0.001341746,0.0009770915,0.001131109,0.003037786,0.001443186,0.002607156],"category_scores_gemma":[0.01522669,0.0009077789,0.001418188,0.001788751,0.002574544,0.002409704,0.001426645,0.003152698,0.0009446794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001109635,"about_ca_system_score_gemma":0.001417754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003875855,"about_ca_topic_score_gemma":0.005083343,"domain_scores_codex":[0.9972747,0.001559324,0.0001107039,0.0003452401,0.0006155398,0.00009452736],"domain_scores_gemma":[0.9941672,0.004212309,0.0002691146,0.0007366181,0.0004803827,0.0001344228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003358115,0.0001883241,0.002725339,0.0003743775,0.000312817,0.0003797259,0.000483751,0.3182641,0.008812643,0.471162,0.005004164,0.1919569],"study_design_scores_gemma":[0.00006655653,0.00007946372,0.0003810336,0.00002791454,0.00003876145,0.0001317339,0.00001932883,0.8837683,0.003554738,0.1088418,0.003023734,0.00006668176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001482534,0.00007791394,0.9979717,0.00004007787,0.00001654045,0.00003706417,0.00001693531,0.0001779085,0.0001793719],"genre_scores_gemma":[0.0729252,0.0003162187,0.9239768,0.0001730556,0.00009030824,0.0004183562,0.000205168,0.0002677036,0.001627144],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005253581,"threshold_uncertainty_score":0.02778393,"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."}}