{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002453913,0.0008369692,0.001471428,0.001503197,0.0006590318,0.001817485,0.002679905,0.001744486,0.001907834],"category_scores_gemma":[0.007273285,0.0007426458,0.001340382,0.001797317,0.001585545,0.002659573,0.001759709,0.00164262,0.000647784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001254151,"about_ca_system_score_gemma":0.0009137065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002706116,"about_ca_topic_score_gemma":0.002028201,"domain_scores_codex":[0.9982994,0.0006227143,0.00006864738,0.0004417746,0.0004370698,0.0001303218],"domain_scores_gemma":[0.9976419,0.001426261,0.0003300585,0.0002581346,0.000250931,0.00009272688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006002808,0.00004683269,0.001648627,0.0001565772,0.0001113206,0.0002690895,0.0002323825,0.4499007,0.003939934,0.4878844,0.001855611,0.05389446],"study_design_scores_gemma":[0.000004969624,0.000008946893,0.0001632365,0.000008122385,0.000007642816,0.00004894816,0.000008200022,0.9347941,0.0003749594,0.06338783,0.001175591,0.0000174223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002818687,0.0001616505,0.9961851,0.0001246915,0.00002036007,0.00001129461,0.00003253833,0.00009125384,0.0005543647],"genre_scores_gemma":[0.4326451,0.001217803,0.5558063,0.0003221216,0.0002200699,0.0004248128,0.0006624499,0.0002489572,0.008452267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002706116,"threshold_uncertainty_score":0.01297766,"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."}}