{"id":"W4396747415","doi":"10.3390/axioms13050307","title":"A Short Note on Generating a Random Sample from Finite Mixture Distributions","year":2024,"lang":"en","type":"article","venue":"Axioms","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sample (material); Mathematics; Statistics; Statistical physics; Physics; Thermodynamics","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":[0.0002987114,0.0001611628,0.0001786086,0.00006614671,0.0001662099,0.0003921496,0.0004084044,0.00009730997,0.00002696096],"category_scores_gemma":[0.000155505,0.0001274128,0.0001345077,0.0003875371,0.00002627186,0.0002043007,0.0001186378,0.0002592285,0.00006437372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003721181,"about_ca_system_score_gemma":0.00005595673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001171405,"about_ca_topic_score_gemma":0.00001874267,"domain_scores_codex":[0.9987447,0.000113405,0.0001922038,0.0004912422,0.0001977578,0.0002607283],"domain_scores_gemma":[0.9983997,0.0009496716,0.00001813034,0.0005005054,0.00002965539,0.0001023204],"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":[0.00001056904,0.00003554288,0.000005191968,0.000009993037,0.00004306967,0.00007539425,0.001048672,0.0001455112,0.004613951,0.3763842,0.001999246,0.6156287],"study_design_scores_gemma":[0.0003170526,0.00005374106,0.0001144499,0.0001063719,0.00002570284,0.000008719867,0.000003318747,0.8977837,0.004597534,0.07823351,0.01846926,0.0002866278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001918912,0.000711131,0.9939311,0.001415856,0.0009034474,0.0001320332,0.000208207,0.0002817301,0.000497618],"genre_scores_gemma":[0.5734265,0.00001686195,0.4254687,0.0004843699,0.000375791,0.00002730815,0.00006700078,0.00001164136,0.0001218247],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8976382,"threshold_uncertainty_score":0.5195742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02027094068693593,"score_gpt":0.2905169388670747,"score_spread":0.2702459981801388,"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."}}