{"id":"W2564000705","doi":"10.1109/dsaa.2016.22","title":"Infinite Langevin Mixture Modeling and Feature Selection","year":2016,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Markov chain Monte Carlo; Cluster analysis; Prior probability; Computer science; Feature selection; Mixture model; Model selection; Artificial intelligence; Bayesian probability; Bayesian inference; Feature (linguistics); Algorithm; Posterior probability; Pattern recognition (psychology); Machine learning","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":[0.0001920397,0.00008806813,0.00008833784,0.0000539568,0.00005938027,0.0000678217,0.0001656407,0.00008895724,0.00001029292],"category_scores_gemma":[0.00002125606,0.00004872775,0.00002384538,0.000141057,0.00001016468,0.0003341302,0.00007991719,0.00007771768,0.000007163264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000112072,"about_ca_system_score_gemma":0.00001728899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007535737,"about_ca_topic_score_gemma":0.0000130054,"domain_scores_codex":[0.9993893,0.00004987,0.00006818933,0.0002500315,0.00008860532,0.0001539887],"domain_scores_gemma":[0.99965,0.00004297183,0.00001955708,0.0001758532,0.0000435995,0.00006801724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006285703,0.0000144769,0.0002998547,0.00001209686,0.00001461211,0.000003516287,0.000277606,0.00001071759,0.03040354,0.3183667,0.00857723,0.6420133],"study_design_scores_gemma":[0.0008784081,0.0001249056,0.0005624982,0.0001064163,0.00001473206,0.0001523615,0.000007387631,0.7500633,0.007238501,0.2084639,0.03179295,0.0005945958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002827278,0.0001527972,0.9858347,0.006083007,0.00008955584,0.00005537139,6.369036e-7,0.0001493889,0.004807311],"genre_scores_gemma":[0.2460596,0.00006279868,0.7491759,0.0006934335,0.00008702548,0.000003884588,1.989526e-7,0.000005842868,0.003911281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7500526,"threshold_uncertainty_score":0.198706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01513338469395213,"score_gpt":0.2496538877667034,"score_spread":0.2345205030727513,"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."}}