{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002933646,0.0001663657,0.0002895915,0.00004873374,0.00006925487,0.00006139855,0.0007875448,0.0001044736,0.0000365355],"category_scores_gemma":[0.00004181123,0.0001355994,0.0001193415,0.0003686646,0.00002829403,0.0004612608,0.0002863763,0.0002805279,0.00001017309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007747487,"about_ca_system_score_gemma":0.00005597756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002685662,"about_ca_topic_score_gemma":0.000001273648,"domain_scores_codex":[0.9985789,0.0001865551,0.0002850231,0.0004445967,0.0002405742,0.0002643725],"domain_scores_gemma":[0.9991658,0.00007686195,0.0001142675,0.0003452885,0.00008941661,0.0002083869],"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":[0.000008256104,0.00002254997,0.00002579467,0.00002770051,0.00002252038,0.000008911707,0.002111397,0.001706975,0.006428909,0.9274684,0.0003802663,0.06178834],"study_design_scores_gemma":[0.0002770421,0.0001019736,0.0000369692,0.00001213213,0.000008084663,0.000003839626,0.00001204925,0.9477585,0.007518745,0.04258886,0.001504905,0.0001768974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001861384,0.0001105369,0.9728792,0.004313732,0.00008228987,0.0001185804,0.000001015006,0.0002037033,0.02210481],"genre_scores_gemma":[0.420605,0.000006150683,0.5781763,0.0008173832,0.00004580997,0.000002365396,8.213515e-7,0.000009124216,0.0003369958],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9460515,"threshold_uncertainty_score":0.5529581,"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."}}