{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000452016,0.0001925164,0.0002447444,0.0001281807,0.0001351854,0.0001957231,0.0005287371,0.0001313176,0.00004498652],"category_scores_gemma":[0.0002208716,0.0001607307,0.0001578519,0.001422157,0.00003874403,0.0001888609,0.0001585686,0.0002887413,0.00003052532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007183172,"about_ca_system_score_gemma":0.0002473556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003948247,"about_ca_topic_score_gemma":0.00000272822,"domain_scores_codex":[0.9979945,0.0002552476,0.0002499638,0.0006688953,0.0004357266,0.0003956669],"domain_scores_gemma":[0.9984008,0.000283037,0.00004696376,0.0008992606,0.0001224545,0.0002474922],"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":[0.0000183497,0.0003739428,0.00006334781,0.00001378084,0.0000101023,0.00008032792,0.00003407066,0.00471743,0.0005060455,0.7642666,0.00373125,0.2261848],"study_design_scores_gemma":[0.0003934872,0.00005461758,0.0002754976,0.00001352528,0.000006362159,0.00001041202,6.307887e-7,0.9219635,0.003311917,0.07323352,0.0005355934,0.0002009473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003683184,0.00003392181,0.9777692,0.004394153,0.0001419691,0.0001148514,0.00001504509,0.0001988709,0.01696368],"genre_scores_gemma":[0.3934397,0.000003452036,0.6037156,0.002091313,0.00003271937,0.00001270551,0.00002304082,0.000007523829,0.0006739592],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.917246,"threshold_uncertainty_score":0.6554407,"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."}}