{"id":"W3086637446","doi":"10.1109/iri49571.2020.00025","title":"Fully Bayesian Learning of Multivariate Beta Mixture Models","year":2020,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Markov chain Monte Carlo; Gibbs sampling; Computer science; Artificial intelligence; Conjugate prior; Mixture model; Cluster analysis; Multivariate statistics; Machine learning; Monte Carlo method; Bayesian probability; Bayesian inference; Pattern recognition (psychology); Prior probability; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002990815,0.001052199,0.001588144,0.001212296,0.0006673037,0.001523934,0.002130524,0.001482416,0.002413388],"category_scores_gemma":[0.008760079,0.0009370731,0.001336053,0.001367465,0.001000507,0.00250039,0.001723632,0.002332647,0.001103174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008090633,"about_ca_system_score_gemma":0.001205577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00507522,"about_ca_topic_score_gemma":0.005786739,"domain_scores_codex":[0.9986551,0.0006305695,0.00005166546,0.0002628053,0.0002752921,0.0001246412],"domain_scores_gemma":[0.9976395,0.001491601,0.0001888095,0.0002360531,0.0003573967,0.00008657315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002687555,0.000108312,0.003182161,0.0001585112,0.000168345,0.0001617665,0.0002463435,0.688669,0.003982575,0.0958403,0.00452383,0.2026901],"study_design_scores_gemma":[0.000005491705,0.000008558869,0.0001749881,0.000007032403,0.000008370911,0.00002858379,0.000007932528,0.9807523,0.0003434184,0.01803615,0.0006169484,0.00001024032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004787072,0.0001672763,0.9942071,0.00009060674,0.00001275087,0.00001483522,0.00004804567,0.0001684759,0.0005037711],"genre_scores_gemma":[0.4607742,0.00124399,0.5295889,0.0003375609,0.0001957022,0.000289208,0.001267249,0.0003276317,0.005975609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00507522,"threshold_uncertainty_score":0.01581711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0278272814893458,"score_gpt":0.2619372086326765,"score_spread":0.2341099271433307,"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."}}