{"id":"W2123094878","doi":"","title":"Tree-Structured Stick Breaking for Hierarchical Data","year":2010,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Markov chain Monte Carlo; Hierarchical database model; Tree structure; Cluster analysis; Tree (set theory); Hierarchical clustering; Bayesian inference; Data mining; Algorithm; Bayesian probability; Theoretical computer science; Mathematics; Artificial intelligence; Binary tree","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":[0.012914,0.0009441898,0.002173456,0.003146392,0.001987772,0.003459211,0.004555115,0.003922956,0.00472006],"category_scores_gemma":[0.06182085,0.002076347,0.003143184,0.003826822,0.005090377,0.01046882,0.004189073,0.007394872,0.001039495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003295386,"about_ca_system_score_gemma":0.001946051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009372181,"about_ca_topic_score_gemma":0.009197035,"domain_scores_codex":[0.9939315,0.002285146,0.0004198386,0.001691001,0.001325306,0.0003472839],"domain_scores_gemma":[0.9562507,0.02936709,0.003816841,0.00804866,0.001503912,0.001012796],"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":[0.0001154296,0.00006081293,0.003819789,0.0001972046,0.0001270911,0.000308021,0.0006491812,0.2140475,0.001764753,0.7357679,0.002242276,0.04090016],"study_design_scores_gemma":[0.00001675564,0.00002065211,0.0005375519,0.00003125647,0.00001726013,0.00007174601,0.00002767989,0.6515962,0.0003665053,0.3460554,0.001232691,0.00002632771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005832843,0.0001917337,0.9930058,0.0002194026,0.00001974826,0.00003288411,0.0001580013,0.000196914,0.0003427036],"genre_scores_gemma":[0.3817028,0.001111762,0.6101058,0.0005781553,0.0002572617,0.0005234049,0.001610678,0.0003210714,0.003789183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.012914,"threshold_uncertainty_score":0.06829661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03733069654240435,"score_gpt":0.3100610288616286,"score_spread":0.2727303323192243,"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."}}