{"id":"W2790993801","doi":"10.1002/sta4.177","title":"Flexible clustering of high‐dimensional data via mixtures of joint generalized hyperbolic distributions","year":2018,"lang":"en","type":"article","venue":"Stat","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; McMaster University","funders":"Canada Research Chairs","keywords":"Identifiability; Cluster analysis; Bayesian information criterion; Computer science; Joint (building); Limiting; Mixture model; Determining the number of clusters in a data set; Mathematics; Selection (genetic algorithm); Subspace topology; Applied mathematics; Algorithm; Data mining; Statistics; Artificial intelligence; CURE data clustering algorithm; Correlation clustering; Engineering","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":[0.008688857,0.001549899,0.001748681,0.003559913,0.001175259,0.003625385,0.003183946,0.001878037,0.001994403],"category_scores_gemma":[0.01707255,0.00151014,0.002549257,0.003512874,0.00270925,0.003580418,0.006345347,0.003133126,0.001337995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416811,"about_ca_system_score_gemma":0.001747576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002431843,"about_ca_topic_score_gemma":0.003315741,"domain_scores_codex":[0.9944653,0.002911718,0.0003104115,0.001021579,0.001095606,0.0001954704],"domain_scores_gemma":[0.9928268,0.003882155,0.0007863704,0.001441581,0.0008172811,0.0002459135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002485349,0.0001093402,0.004376802,0.0005488977,0.0004035784,0.0003783786,0.001345121,0.4748079,0.01905872,0.2041444,0.005033912,0.2895444],"study_design_scores_gemma":[0.00001270845,0.00002949713,0.0006885633,0.00003507712,0.00002333598,0.0001556302,0.00007547686,0.9139186,0.00273457,0.0788843,0.003372351,0.00006985117],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001645459,0.00008704713,0.9977987,0.0000496166,0.000009715621,0.00002786182,0.0000367065,0.0001649267,0.0001798398],"genre_scores_gemma":[0.0686253,0.000325702,0.9286262,0.0001037755,0.00003702869,0.0002658766,0.000449021,0.0002796492,0.001287406],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008688857,"threshold_uncertainty_score":0.0459516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05689455068376795,"score_gpt":0.3112572861862972,"score_spread":0.2543627355025292,"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."}}