{"id":"W2775197842","doi":"10.48550/arxiv.1712.02750","title":"A Convergence Diagnostic for Bayesian Clustering","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Cluster analysis; Bayesian probability; Markov chain; Computer science; Posterior probability; Gibbs sampling; Convergence (economics); State space; Sampling (signal processing); Mathematical optimization; Mathematics; Machine learning; Artificial intelligence; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02133963,0.001069781,0.001698529,0.005255141,0.001881446,0.002263319,0.003336292,0.0037167,0.004612474],"category_scores_gemma":[0.1288512,0.0006797616,0.00162921,0.002204316,0.005773769,0.004377949,0.004441752,0.004041055,0.001182442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002646573,"about_ca_system_score_gemma":0.002384942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002383975,"about_ca_topic_score_gemma":0.001289661,"domain_scores_codex":[0.9913975,0.005050512,0.00036208,0.001213981,0.001580039,0.000395824],"domain_scores_gemma":[0.9276514,0.05672582,0.004112382,0.004032338,0.006073639,0.001404338],"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":[0.0001238137,0.00004754417,0.005644032,0.0001461088,0.00006440374,0.0001951385,0.0004436706,0.08451024,0.001191451,0.8696195,0.00305485,0.03495927],"study_design_scores_gemma":[0.00002345778,0.00004265743,0.0006937645,0.00008151978,0.00001356087,0.0001607915,0.00006602486,0.512849,0.0009406769,0.4831955,0.001883471,0.00004948368],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01526682,0.0003190822,0.9805194,0.0006843949,0.00005276073,0.00006913455,0.0001323547,0.0003460831,0.002610059],"genre_scores_gemma":[0.5499976,0.000491397,0.4435286,0.0007872173,0.0002759287,0.0005077143,0.0007738701,0.0005021368,0.003135618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02133963,"threshold_uncertainty_score":0.1128561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09424591298712023,"score_gpt":0.2295658257888058,"score_spread":0.1353199128016856,"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."}}