{"id":"W2050627451","doi":"10.1007/s11222-006-8451-7","title":"Practical Bayesian estimation of a finite beta mixture through gibbs sampling and its applications","year":2006,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Gibbs sampling; Posterior probability; Mixture model; Estimator; Histogram; Bayesian probability; Artificial intelligence; Beta distribution; Pattern recognition (psychology); Mathematics; Computer science; Sampling (signal processing); Conditional probability distribution; Statistics; Image (mathematics)","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":[0.005679901,0.001200705,0.001981702,0.001718752,0.001025709,0.002136847,0.002809015,0.002187904,0.003792881],"category_scores_gemma":[0.03164988,0.001466359,0.001111378,0.002313874,0.002791223,0.00401751,0.003147728,0.002914102,0.001005525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258915,"about_ca_system_score_gemma":0.001589066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006351621,"about_ca_topic_score_gemma":0.005565711,"domain_scores_codex":[0.9976338,0.001426002,0.0000768951,0.000337226,0.0004269602,0.00009900796],"domain_scores_gemma":[0.9870004,0.01110243,0.0003729912,0.0006593692,0.0006702141,0.0001946268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001227709,0.00006956567,0.0009883227,0.0001773603,0.00008744067,0.0001042368,0.0002676353,0.5495484,0.001169065,0.3689824,0.002342513,0.07614034],"study_design_scores_gemma":[0.000009509348,0.000006747281,0.00009560083,0.0000153985,0.000008030387,0.00003410983,0.00001114091,0.8731269,0.0002165505,0.1255542,0.0009062196,0.00001563367],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001554913,0.0001896021,0.9976403,0.00009518041,0.00001070697,0.000007005301,0.00001309815,0.00006223242,0.0004270318],"genre_scores_gemma":[0.1503807,0.001613141,0.843101,0.0001767932,0.0002005775,0.0002235531,0.0003361512,0.0003282643,0.003639748],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006351621,"threshold_uncertainty_score":0.0300386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02906756747162057,"score_gpt":0.333769758462675,"score_spread":0.3047021909910544,"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."}}