{"id":"W3216183861","doi":"10.1109/iri51335.2021.00015","title":"A Hierarchical Nonparametric Bayesian Model Based on Scaled Dirichlet Distribution","year":2021,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Dirichlet distribution; Cluster analysis; Hierarchical Dirichlet process; Artificial intelligence; Machine learning; Inference; Dirichlet process; Mixture model; Data mining; Flexibility (engineering); Hierarchical clustering; Bayesian inference; Unsupervised learning; Bayesian probability; Domain (mathematical analysis); Topic model; Latent Dirichlet allocation; Mathematics; Statistics","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":[0.00434968,0.0009091811,0.002103533,0.002034594,0.001047365,0.002344635,0.003729417,0.002571664,0.004662849],"category_scores_gemma":[0.0115559,0.0009468113,0.001781227,0.002545915,0.002129784,0.003388108,0.001574243,0.002727162,0.001208791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001821477,"about_ca_system_score_gemma":0.001761248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01219209,"about_ca_topic_score_gemma":0.01257521,"domain_scores_codex":[0.9967749,0.001613276,0.0001274058,0.0007021735,0.0005603851,0.0002219648],"domain_scores_gemma":[0.9961485,0.002819631,0.0002866442,0.0002286268,0.0003970213,0.0001196083],"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":[0.0001392908,0.00007758855,0.001906759,0.0001920168,0.0001395725,0.0002354222,0.000421895,0.5144942,0.001430916,0.4211404,0.005245295,0.05457678],"study_design_scores_gemma":[0.00002406616,0.00001572737,0.0002957667,0.0000220013,0.00002178597,0.00006473606,0.00002538139,0.890633,0.0001885038,0.1066002,0.002083314,0.00002556831],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005769071,0.0003754534,0.9906387,0.0005427353,0.00005470737,0.00006069053,0.0002821894,0.0001982302,0.002078058],"genre_scores_gemma":[0.5082185,0.001972734,0.4684852,0.0007990074,0.0004679484,0.0008974362,0.001658369,0.0002744501,0.01722633],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01219209,"threshold_uncertainty_score":0.02424228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0147331209690797,"score_gpt":0.2682840225351069,"score_spread":0.2535509015660272,"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."}}