{"id":"W2082905687","doi":"10.1007/s11634-013-0152-4","title":"Infinite Dirichlet mixture models learning via expectation propagation","year":2013,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Dirichlet process; Mixture model; Model selection; Dirichlet distribution; Computer science; Hierarchical Dirichlet process; Inference; Automatic summarization; Cluster analysis; Bayesian inference; Minimum description length; Artificial intelligence; Latent Dirichlet allocation; Data mining; Selection (genetic algorithm); Synthetic data; Machine learning; Bayesian probability; Topic model; Mathematics","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":[0.009573735,0.001870275,0.00482599,0.003270885,0.001611129,0.004529855,0.006645836,0.003988808,0.004819538],"category_scores_gemma":[0.03479207,0.002593705,0.003377579,0.004297863,0.003481496,0.008995174,0.00505566,0.0071494,0.002241221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002411637,"about_ca_system_score_gemma":0.002277368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006404571,"about_ca_topic_score_gemma":0.009121533,"domain_scores_codex":[0.9926249,0.004136672,0.0003628814,0.001350019,0.001166619,0.0003588374],"domain_scores_gemma":[0.9733279,0.02263919,0.0007508483,0.001583438,0.001395915,0.0003026846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003472658,0.0002169459,0.001558091,0.0003685621,0.0003328364,0.0001374787,0.0004615,0.4553556,0.0009176211,0.352229,0.007576832,0.1804982],"study_design_scores_gemma":[0.00001970947,0.00001044046,0.0001038736,0.00002810284,0.00002429293,0.00003078115,0.00001342922,0.83741,0.0002479044,0.1610682,0.001018398,0.00002484266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001668642,0.0003744709,0.9970862,0.0002222114,0.00002806746,0.00001701212,0.00005878383,0.0001780583,0.0003664835],"genre_scores_gemma":[0.2049044,0.00255463,0.7782052,0.0008312665,0.0007445607,0.0007768663,0.001901872,0.0005728711,0.009508403],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009573735,"threshold_uncertainty_score":0.0506314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03051917684331791,"score_gpt":0.3035554330616236,"score_spread":0.2730362562183057,"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."}}