{"id":"W4413182436","doi":"10.1214/25-aap2202","title":"Unconditional large deviation principles for Dirichlet posterior and Bayesian bootstrap","year":2025,"lang":"en","type":"article","venue":"The Annals of Applied Probability","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Dirichlet distribution; Bayesian probability; Mathematics; Latent Dirichlet allocation; Statistics; Econometrics; Computer science; Artificial intelligence; Topic model; 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":[0.00185249,0.0001159198,0.0001980152,0.00004516236,0.0001598208,0.00005496869,0.0004276768,0.00006301612,0.000004162379],"category_scores_gemma":[0.00006988374,0.00008218556,0.00006922512,0.000189365,0.00009550506,0.0001163062,0.000197978,0.00007748904,5.218055e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009242316,"about_ca_system_score_gemma":0.00009753405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002914678,"about_ca_topic_score_gemma":0.000009264128,"domain_scores_codex":[0.9989594,0.00006981259,0.0003046744,0.0003270731,0.0001288888,0.0002101706],"domain_scores_gemma":[0.9989114,0.0002724116,0.0001334444,0.0005010812,0.0001400561,0.00004159551],"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":[0.00005948902,0.00006818977,0.0001318775,0.0001230039,0.00002426466,5.630149e-8,0.0002543323,0.00001552085,0.0005484014,0.9606274,0.000377498,0.03776992],"study_design_scores_gemma":[0.000271894,0.00004097369,0.01174002,0.00001785125,0.00001079011,7.186601e-7,0.000007791329,0.004944118,0.01022777,0.9691165,0.003531047,0.00009054603],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03693271,0.0001006728,0.9515759,0.007347667,0.00005309299,0.0008169875,0.00005289818,0.00003957656,0.003080538],"genre_scores_gemma":[0.7214141,0.000008855252,0.2769173,0.001419773,0.00002533721,0.0001352017,0.00001196008,0.000004138443,0.00006337914],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6844814,"threshold_uncertainty_score":0.335143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0836380330588424,"score_gpt":0.3468204848049025,"score_spread":0.26318245174606,"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."}}