{"id":"W2766659256","doi":"10.5539/ijsp.v7n1p21","title":"Characterization of the Bayesian Posterior Distribution in Terms of Self-information","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Mechanics and Entropy","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Posterior probability; Bayesian probability; Bayesian experimental design; Mathematics; Minimax; Bayes factor; Prior probability; Bayesian hierarchical modeling; Bayesian linear regression; Bayesian statistics; Bayes' theorem; Bayesian average; Bayes estimator; Bayesian inference; Applied mathematics; Statistics; Mathematical optimization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002247095,0.00004987366,0.0001220058,0.00002199783,0.0000389154,0.00004901487,0.0002018189,0.00001708208,0.00003613597],"category_scores_gemma":[0.0001152183,0.00003548649,0.00003268967,0.00001727156,0.00005469846,0.0002206334,0.00005704701,0.00007137434,1.891658e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002442447,"about_ca_system_score_gemma":0.00003303561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000593772,"about_ca_topic_score_gemma":0.000002635813,"domain_scores_codex":[0.9992419,0.00002586415,0.0004453548,0.00004129004,0.0001937899,0.00005183462],"domain_scores_gemma":[0.9987198,0.00004393653,0.0008059369,0.00009998111,0.0003060829,0.00002430808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000106866,0.0001486455,0.3298085,0.00004745981,0.00006028462,7.368945e-7,0.0002699128,0.00000855178,0.002289355,0.5980017,0.00001770716,0.06924025],"study_design_scores_gemma":[0.000550728,0.0000667919,0.8293269,0.00008000661,0.00001884667,0.000002882164,0.00001518701,0.006318845,0.002349197,0.161038,0.0001863218,0.0000463008],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7405121,9.581859e-7,0.2568531,0.0002591,0.0003502373,0.00008890497,0.001796968,6.360656e-7,0.0001378944],"genre_scores_gemma":[0.9974723,0.000005339109,0.002421603,0.000006754439,0.00004207551,0.000001130486,0.0000468009,0.000001711518,0.000002220961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4995184,"threshold_uncertainty_score":0.1447097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00644957818264588,"score_gpt":0.2478602881227408,"score_spread":0.2414107099400949,"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."}}