{"id":"W3098230082","doi":"10.1002/wics.1536","title":"A convergence diagnostic for Bayesian clustering","year":2020,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); McGill University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Cluster analysis; Computer science; Gibbs sampling; Posterior probability; Bayesian probability; Data mining; Markov chain; Machine learning; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000796949,0.0009417115,0.003204216,0.0002115126,0.0003824976,0.0003148908,0.002074437,0.0002555966,0.00004988286],"category_scores_gemma":[0.0007588724,0.0007997355,0.0009060996,0.0006402902,0.00011061,0.000297618,0.002010345,0.00056278,0.0002599303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002303116,"about_ca_system_score_gemma":0.0005094734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001314321,"about_ca_topic_score_gemma":0.000003998739,"domain_scores_codex":[0.9948328,0.000710114,0.002111877,0.001357289,0.0004097782,0.0005781387],"domain_scores_gemma":[0.9925461,0.004894238,0.00119269,0.0007421148,0.0002183118,0.0004065035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003099065,0.00004335718,1.52178e-7,0.01546144,0.00009560206,0.00006837393,0.000198483,0.00007357789,1.268301e-8,0.04609177,0.03188846,0.9060757],"study_design_scores_gemma":[0.000136411,0.0001768885,3.450247e-7,0.01258228,0.000318721,0.0001607987,0.000001952512,0.1769456,1.864633e-8,0.08171491,0.7273127,0.0006492925],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[5.30216e-10,0.4932404,0.5034195,0.0001234303,0.0007455313,0.001684675,0.0006330413,0.00007720043,0.00007631805],"genre_scores_gemma":[1.5231e-7,0.5069298,0.4914987,0.0001520121,0.0002601663,0.0005946432,0.0004501007,0.00005094941,0.00006351413],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9054264,"threshold_uncertainty_score":0.9994454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06919171479297738,"score_gpt":0.3906654161054129,"score_spread":0.3214737013124356,"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."}}