{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01194338,0.0006853614,0.001257226,0.005132504,0.0005957761,0.001781449,0.002227003,0.002221388,0.002305827],"category_scores_gemma":[0.04891969,0.0005054691,0.0007831256,0.002844642,0.003971928,0.002692147,0.001893973,0.003045064,0.001111813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002635488,"about_ca_system_score_gemma":0.001918939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002192098,"about_ca_topic_score_gemma":0.001132736,"domain_scores_codex":[0.9934248,0.003700237,0.0002634424,0.0006971558,0.001772085,0.0001421726],"domain_scores_gemma":[0.973892,0.01942857,0.001531593,0.001209642,0.003568811,0.000369386],"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":[0.00007886488,0.00002702292,0.002022343,0.0006367057,0.0001000206,0.00009351763,0.0001960908,0.0503185,0.0006380868,0.7391762,0.006822376,0.1998902],"study_design_scores_gemma":[0.00002340209,0.00004233944,0.001143812,0.0004799936,0.00002779139,0.0003292506,0.00005694205,0.3306981,0.001371452,0.6469122,0.01883295,0.00008175184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003775723,0.01363209,0.9737806,0.001476766,0.0002347578,0.00005543933,0.000137033,0.0004083551,0.006499318],"genre_scores_gemma":[0.4114613,0.01736387,0.5624164,0.001405084,0.0009347922,0.0004760296,0.0007701963,0.0004754708,0.004696888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01194338,"threshold_uncertainty_score":0.06316334,"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."}}