{"id":"W2060556149","doi":"10.1007/s10044-008-0111-4","title":"On Bayesian analysis of a finite generalized Dirichlet mixture via a Metropolis-within-Gibbs sampling","year":2008,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; Concordia University","funders":"","keywords":"Dirichlet distribution; Gibbs sampling; Conjugate prior; Mathematics; Hierarchical Dirichlet process; Metropolis–Hastings algorithm; Generalized Dirichlet distribution; Latent Dirichlet allocation; Kernel (algebra); Applied mathematics; Bayesian probability; Pattern recognition (psychology); Computer science; Artificial intelligence; Algorithm; Statistics; Posterior probability; Markov chain Monte Carlo; Topic model; Dirichlet's energy; Combinatorics; Mathematical analysis","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":[0.0003479118,0.0002265118,0.0007208004,0.001237613,0.0002833618,0.0000677151,0.0005385085,0.00009374013,0.00005052088],"category_scores_gemma":[0.00001969058,0.0001878675,0.0005425556,0.00585479,0.00008317703,0.000100805,0.0001151199,0.0001526562,0.000004342291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000224125,"about_ca_system_score_gemma":0.00002451405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004388175,"about_ca_topic_score_gemma":0.0002058167,"domain_scores_codex":[0.9980947,0.0001522551,0.0005284224,0.0006693059,0.0003119336,0.0002433701],"domain_scores_gemma":[0.9980926,0.0002353952,0.000330972,0.001040819,0.0001255409,0.0001747106],"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":[0.00003661118,0.001540823,0.09275573,0.0001098019,0.03478738,0.00002675192,0.006506668,0.03677123,0.008125968,0.3863713,0.0004708662,0.4324969],"study_design_scores_gemma":[0.000301823,0.00004081285,0.02029153,0.000007127809,0.005217182,0.00000428215,0.00001279666,0.9595532,0.001345529,0.01215691,0.0006611017,0.0004077376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01034166,0.0001961025,0.9885517,0.0003404028,0.00001510082,0.0001703549,0.00006227932,0.00005254511,0.0002698934],"genre_scores_gemma":[0.765613,0.0001049377,0.2332534,0.0007166491,0.00003925332,0.00008973679,0.00007052508,0.000009472137,0.00010303],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9227819,"threshold_uncertainty_score":0.7661015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02449743636263198,"score_gpt":0.2922775395972536,"score_spread":0.2677801032346216,"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."}}