{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009145683,0.001581145,0.001216258,0.001531436,0.001771334,0.003185618,0.002708442,0.003599158,0.01595408],"category_scores_gemma":[0.04720988,0.00104545,0.001866787,0.002438168,0.00469578,0.007279123,0.003411988,0.01140408,0.007919207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001600714,"about_ca_system_score_gemma":0.001693799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003482736,"about_ca_topic_score_gemma":0.003939479,"domain_scores_codex":[0.9935982,0.003027101,0.0003891044,0.0009592106,0.001909057,0.0001173084],"domain_scores_gemma":[0.9771835,0.0189162,0.0004085193,0.002007519,0.001151143,0.0003331991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000714559,0.00004694692,0.0005113027,0.0002986469,0.00004786926,0.0004486296,0.0004184381,0.00917645,0.001198946,0.8322363,0.05305341,0.1024917],"study_design_scores_gemma":[0.00003017005,0.00005821557,0.000466722,0.0003353386,0.00003284653,0.0005509358,0.00005009209,0.03973399,0.001241734,0.751037,0.2063463,0.0001166684],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004959484,0.002571109,0.9789162,0.005739229,0.002875661,0.00007302024,0.0001545404,0.0005107569,0.008663636],"genre_scores_gemma":[0.02191857,0.007519673,0.9364769,0.005756789,0.01008165,0.0004776863,0.0004586277,0.001093088,0.01621703],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01595408,"threshold_uncertainty_score":0.05337173,"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."}}