{"id":"W3111363863","doi":"10.1109/smc42975.2020.9283007","title":"Variational Inference of Infinite Generalized Gaussian Mixture Models with Feature Selection","year":2020,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Mixture model; Cluster analysis; Inference; Gaussian; Model selection; Feature selection; Selection (genetic algorithm); Computer science; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Gaussian process; Generalized normal distribution; Flexibility (engineering); Algorithm; Mathematics; Mathematical optimization; Normal distribution; Statistics","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":[0.004149417,0.000932384,0.001760833,0.001666079,0.0006692723,0.001547994,0.003139194,0.001505334,0.001550837],"category_scores_gemma":[0.0110784,0.001230491,0.00176559,0.00156345,0.001470247,0.001958486,0.002022039,0.001909434,0.0003541459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001502987,"about_ca_system_score_gemma":0.001448337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00908402,"about_ca_topic_score_gemma":0.007962711,"domain_scores_codex":[0.9981516,0.00104206,0.00006829781,0.0002923031,0.0003283623,0.0001173353],"domain_scores_gemma":[0.9962658,0.002954041,0.0002060015,0.0001832848,0.0002998102,0.00009104417],"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":[0.0000667775,0.00003574604,0.0008922175,0.00008343144,0.0001154343,0.00008475039,0.00008324348,0.8678983,0.0007586109,0.0880431,0.001234421,0.0407039],"study_design_scores_gemma":[0.000003650209,0.000004182104,0.00004857416,0.00000343859,0.00000369718,0.000007437185,0.000002850539,0.9820743,0.00006802194,0.01760024,0.0001785258,0.000004972153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003097195,0.0001584664,0.996256,0.00009382729,0.00001349327,0.00001204122,0.00002502632,0.00007888942,0.0002649936],"genre_scores_gemma":[0.4360088,0.0006909494,0.5578377,0.0002685446,0.0001746077,0.0003300324,0.0007095837,0.0002393298,0.003740468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00908402,"threshold_uncertainty_score":0.02194452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02611439777089712,"score_gpt":0.25834608272426,"score_spread":0.2322316849533629,"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."}}