{"id":"W4388290228","doi":"10.1093/oso/9780198526155.003.0042","title":"Density Modeling and Clustering Using Dirichlet Diffusion Trees","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hierarchical Dirichlet process; Dirichlet distribution; Cluster analysis; Dirichlet process; Mathematics; Hierarchical clustering; Markov chain Monte Carlo; Computer science; Inference; Artificial intelligence; Monte Carlo method; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003029529,0.0003488255,0.0004120724,0.0001463716,0.0001817742,0.0001824465,0.000323047,0.0003001387,0.0000165907],"category_scores_gemma":[0.000008421353,0.0003002267,0.0001012805,0.00003074583,0.0000325498,0.0002223388,0.0005688172,0.0002836116,0.00000328021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004773064,"about_ca_system_score_gemma":0.00003112985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003706218,"about_ca_topic_score_gemma":0.00004316555,"domain_scores_codex":[0.9985233,0.00003767223,0.0002820949,0.0006695123,0.0002320056,0.0002554292],"domain_scores_gemma":[0.9990832,0.00003958574,0.0001036801,0.0005698648,0.00006565518,0.0001380521],"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":[0.000006184293,0.00001431194,0.000007304303,0.00006668964,0.00004538092,0.00006562908,0.0002879023,0.0008300302,0.001050118,0.7904526,0.0001683024,0.2070055],"study_design_scores_gemma":[0.0001133582,0.00001456294,0.000001054461,0.0001074994,0.00002315669,0.00008092575,0.000001015829,0.8661308,0.00003094239,0.1318433,0.00130164,0.0003517719],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000186254,0.0005393518,0.8684031,0.00008776231,0.000244628,0.0001270834,9.769481e-7,0.00009503823,0.1303159],"genre_scores_gemma":[0.001794725,0.0002533665,0.9304947,0.0006597707,0.00009063083,8.925506e-7,0.000001186993,0.00003701533,0.06666776],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8653008,"threshold_uncertainty_score":0.999945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04290611279874024,"score_gpt":0.2609226973229924,"score_spread":0.2180165845242522,"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."}}