{"id":"W2091793380","doi":"10.1002/wics.102","title":"Bayesian inference: an approach to statistical inference","year":2010,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Prior probability; Bayes' theorem; Statistical inference; Frequentist inference; Bayesian probability; Inference; Bayes factor; Mathematics; Computer science; Bayesian inference; Artificial intelligence; Statistics","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":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.002021801,0.00177241,0.005582494,0.0005862416,0.0005832901,0.0005183254,0.001864413,0.0008300523,0.001044031],"category_scores_gemma":[0.007066633,0.001416651,0.0005294152,0.001008519,0.0005321541,0.0002842129,0.001505919,0.002538005,0.0008317214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003133772,"about_ca_system_score_gemma":0.0009521919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001123806,"about_ca_topic_score_gemma":0.000023946,"domain_scores_codex":[0.9895834,0.002134058,0.004002559,0.001984302,0.001168137,0.001127485],"domain_scores_gemma":[0.9823007,0.01278914,0.001483133,0.001489496,0.0005910106,0.001346539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001008516,0.0004150457,0.000001854618,0.00914708,0.00005942795,0.00002846036,0.0001903773,0.000010699,4.403043e-8,0.4099125,0.007683801,0.5725406],"study_design_scores_gemma":[0.0001464102,0.0003523456,0.0000094093,0.008988959,0.000767627,0.0001145163,0.00002708082,0.007703902,2.245885e-8,0.613068,0.3675359,0.001285767],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[8.55984e-8,0.2874609,0.7009503,0.0000168702,0.0005735962,0.002430604,0.006726531,0.0001428272,0.001698258],"genre_scores_gemma":[0.000002041046,0.3994339,0.5962071,0.0000865991,0.0003785997,0.000730977,0.002921171,0.0001530926,0.00008651165],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5712549,"threshold_uncertainty_score":0.9999462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1451709663558175,"score_gpt":0.485227297478791,"score_spread":0.3400563311229735,"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."}}