{"id":"W2544994990","doi":"10.1109/icmla.2011.81","title":"Infinite Dirichlet Mixture Model and Its Application via Variational Bayes","year":2011,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Dirichlet process; Hierarchical Dirichlet process; Dirichlet distribution; Generalized Dirichlet distribution; Latent Dirichlet allocation; Mixture model; Inference; Bayes' theorem; Mathematics; Artificial intelligence; Bayesian inference; Representation (politics); Categorization; Concentration parameter; Applied mathematics; Computer science; Pattern recognition (psychology); Dirichlet's principle; Bayesian probability; Topic model; Mathematical analysis; Boundary value problem","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":[0.006683027,0.0009361905,0.002279001,0.00255164,0.001296709,0.002813992,0.003957756,0.002663271,0.003298974],"category_scores_gemma":[0.01977824,0.001347401,0.001971817,0.002459222,0.002508117,0.003831666,0.002848571,0.003430396,0.0008870913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002311629,"about_ca_system_score_gemma":0.002008198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00858049,"about_ca_topic_score_gemma":0.006431761,"domain_scores_codex":[0.9948047,0.003236765,0.0001505818,0.0006380818,0.0009780584,0.0001917924],"domain_scores_gemma":[0.9943804,0.004571661,0.0002262307,0.0002836332,0.0003955867,0.0001425093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005079805,0.0000441374,0.000758539,0.000161192,0.0001281357,0.0001312742,0.0002967345,0.3507366,0.0007447249,0.5917401,0.002506376,0.05270144],"study_design_scores_gemma":[0.000007317285,0.000007179077,0.00009466585,0.00002141031,0.00001191993,0.00004248777,0.00001517159,0.8040259,0.000189289,0.1932601,0.002304223,0.00002025265],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001188908,0.0004364569,0.9969314,0.0002641556,0.00003301927,0.00001776835,0.00002648638,0.00007659645,0.001025182],"genre_scores_gemma":[0.2214518,0.002123499,0.7666153,0.0004078036,0.0003996071,0.0004658636,0.0003928786,0.0003568352,0.007786308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00858049,"threshold_uncertainty_score":0.03534365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02529748487591747,"score_gpt":0.246881730096876,"score_spread":0.2215842452209585,"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."}}