{"id":"W2008561984","doi":"10.1093/biomet/asm056","title":"Simulation of hyper-inverse Wishart distributions in graphical models","year":2007,"lang":"en","type":"article","venue":"Biometrika","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Science Foundation","keywords":"Wishart distribution; Markov chain Monte Carlo; Graphical model; Mathematics; Inverse; Inverse-Wishart distribution; Context (archaeology); Markov chain; Algorithm; Gibbs sampling; Applied mathematics; Monte Carlo method; Theoretical computer science; Computer science; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005644549,0.00007762564,0.0001227904,0.0007755043,0.00003512708,0.00002666257,0.0003407866,0.00008907756,0.000004981837],"category_scores_gemma":[0.0001248028,0.0000761779,0.00005277199,0.00362796,0.00005337623,0.0003193252,0.00007938467,0.00009517893,0.00001056876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000372439,"about_ca_system_score_gemma":0.00003821548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005370417,"about_ca_topic_score_gemma":0.00001881193,"domain_scores_codex":[0.999027,0.0000237993,0.0002818642,0.0002198368,0.0002290609,0.0002185026],"domain_scores_gemma":[0.9992575,0.0002059849,0.0000598568,0.0002835831,0.0001086797,0.0000844052],"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":[0.00005982667,0.001040558,0.01404899,0.00005696499,0.00003316374,0.00003482133,0.0007505364,0.214769,0.01339071,0.5440085,0.0003750964,0.2114318],"study_design_scores_gemma":[0.0002099595,0.00004631519,0.00486443,0.00001648184,0.000002316698,0.000001190332,0.000007911675,0.9726762,0.001787491,0.0200853,0.0002010001,0.0001014653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1515779,0.00006623103,0.8477098,0.00009682145,0.00008945058,0.00005450418,0.000008578129,0.0000482144,0.0003484478],"genre_scores_gemma":[0.9793296,0.0000083672,0.0205849,0.00003459538,0.00001694519,0.00000153865,0.000007347224,0.000003076173,0.0000135844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8277517,"threshold_uncertainty_score":0.3106444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05460151362738178,"score_gpt":0.3021830003647726,"score_spread":0.2475814867373909,"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."}}