{"id":"W2040968446","doi":"10.1007/s11042-012-1191-0","title":"Variational learning for Dirichlet process mixtures of Dirichlet distributions and applications","year":2012,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Hierarchical Dirichlet process; Overfitting; Dirichlet process; Computer science; Dirichlet distribution; Inference; Artificial intelligence; Markov chain Monte Carlo; Latent Dirichlet allocation; Machine learning; Mixture model; Mathematical optimization; Algorithm; Applied mathematics; Topic model; Bayesian probability; Mathematics; Artificial neural network; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003280057,0.0001342076,0.0001817432,0.00005973596,0.000358331,0.00008056195,0.0002369717,0.00008469396,0.000004577322],"category_scores_gemma":[0.00008730606,0.0001213413,0.00004426749,0.0003031047,0.0001013255,0.0003698856,0.0000882511,0.0001186213,0.000002502503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001156843,"about_ca_system_score_gemma":0.00004007589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004440235,"about_ca_topic_score_gemma":7.446741e-7,"domain_scores_codex":[0.9990293,0.00003909802,0.0002537565,0.0002994714,0.0001274474,0.0002508755],"domain_scores_gemma":[0.9986832,0.0005784316,0.0001489009,0.00025688,0.0001617371,0.0001708772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000328064,0.0001951437,0.002555447,0.00008231852,0.0000272308,2.784787e-8,0.0005179753,0.00002142536,0.001947638,0.6036054,0.0002255706,0.3908185],"study_design_scores_gemma":[0.002283956,0.0001536018,0.09364234,0.00006338287,0.0002866127,0.0000450298,0.0001792163,0.1708226,0.00923431,0.2911139,0.4308087,0.001366458],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004261026,0.0009614324,0.9963031,0.000578208,0.00002809129,0.0009940488,0.0002053402,0.00006709625,0.0004365399],"genre_scores_gemma":[0.3551224,0.0001121903,0.6403631,0.00008527549,0.0003377422,0.003645423,0.0002167234,0.00001240474,0.0001046866],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4305831,"threshold_uncertainty_score":0.4948156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02221440058698505,"score_gpt":0.3017507632421791,"score_spread":0.2795363626551941,"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."}}