{"id":"W1853714018","doi":"10.1007/s10489-015-0714-6","title":"Variational Bayesian inference for infinite generalized inverted Dirichlet mixtures with feature selection and its application to clustering","year":2015,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dirichlet distribution; Computer science; Dirichlet process; Hierarchical Dirichlet process; Cluster analysis; Prior probability; Artificial intelligence; Pattern recognition (psychology); Model selection; Inference; Bayesian inference; Feature selection; Generalized Dirichlet distribution; Feature (linguistics); Latent Dirichlet allocation; Selection (genetic algorithm); Bayesian probability; Data mining; Algorithm; Dirichlet's energy; Topic model; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01145464,0.001599738,0.003936447,0.003209262,0.001974167,0.003894188,0.007272018,0.003791301,0.003356733],"category_scores_gemma":[0.05290057,0.002563637,0.003543257,0.004602377,0.004642783,0.006051562,0.004641342,0.005458472,0.0007177619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003324545,"about_ca_system_score_gemma":0.003170931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01551408,"about_ca_topic_score_gemma":0.01601762,"domain_scores_codex":[0.9941968,0.003603827,0.0002832154,0.0007756535,0.000874092,0.0002664099],"domain_scores_gemma":[0.9659835,0.03004885,0.0008000123,0.001315992,0.001529667,0.0003219732],"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":[0.0001361968,0.0000970183,0.0007844772,0.0002319784,0.000218444,0.00007942393,0.0003058435,0.5669011,0.0005268226,0.378653,0.002544319,0.04952131],"study_design_scores_gemma":[0.0000096739,0.000005696013,0.00007799509,0.00001418002,0.00001205371,0.00001328136,0.00001133719,0.867754,0.00009994093,0.1315358,0.0004493246,0.00001675004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00217304,0.0003730628,0.9967017,0.0001977446,0.00002203762,0.00002114401,0.0000425224,0.000067898,0.0004008174],"genre_scores_gemma":[0.2140751,0.001889485,0.7752934,0.0004316838,0.0004335863,0.0005944853,0.0009341887,0.0003772884,0.005970768],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01551408,"threshold_uncertainty_score":0.06057864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03030466808094225,"score_gpt":0.2935755336905591,"score_spread":0.2632708656096168,"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."}}