{"id":"W4313564281","doi":"10.1109/icit48603.2022.10002752","title":"Bayesian Model and Feature Selection in Asymmetric Generalized Gaussian Mixtures","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Industrial Technology (ICIT)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Feature selection; Computer science; Gaussian process; Bayesian probability; Gaussian; Artificial intelligence; Selection (genetic algorithm); Model selection; Pattern recognition (psychology); Feature (linguistics); Machine learning; Algorithm; Chemistry","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":[0.003116075,0.0008014765,0.001363327,0.001518487,0.00059885,0.001341624,0.00232496,0.001461362,0.001187119],"category_scores_gemma":[0.008724369,0.0006977604,0.001237544,0.001513072,0.001219085,0.001879732,0.001278563,0.001667287,0.0004349764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009606032,"about_ca_system_score_gemma":0.001030275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006293282,"about_ca_topic_score_gemma":0.005256604,"domain_scores_codex":[0.997986,0.0008864971,0.00007687516,0.0003950209,0.0005090763,0.0001465441],"domain_scores_gemma":[0.9976246,0.001612417,0.0002164588,0.0001822055,0.0003002188,0.00006420932],"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":[0.0001287955,0.00007179319,0.001940733,0.0000947325,0.0001134668,0.0001376638,0.0001340817,0.8135058,0.002882541,0.08260526,0.00129527,0.09708973],"study_design_scores_gemma":[0.000005726254,0.000009052543,0.0001778442,0.000004266831,0.000006595758,0.00001899543,0.000004064021,0.9844055,0.0002464523,0.01480945,0.0003040611,0.000008001596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006595059,0.0001360114,0.9927246,0.00009247545,0.00001188246,0.00001911127,0.00003102666,0.0001068379,0.0002829887],"genre_scores_gemma":[0.5443151,0.0006523012,0.4504823,0.0002313126,0.0001316773,0.000351459,0.0004701229,0.0001327534,0.003232955],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006293282,"threshold_uncertainty_score":0.01647955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06199279767613432,"score_gpt":0.3106579513614001,"score_spread":0.2486651536852658,"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."}}