{"id":"W2766339011","doi":"10.3390/e19100564","title":"Prior Elicitation, Assessment and Inference with a Dirichlet Prior","year":2017,"lang":"en","type":"article","venue":"Entropy","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Falsity; Prior probability; Contingency table; Dirichlet distribution; Inference; Prior information; Contingency; Computer science; Mathematics; Econometrics; Statistics; Artificial intelligence; Machine learning; Bayesian probability; Epistemology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01891317,0.001687673,0.002150742,0.004494416,0.001885792,0.004569346,0.003126398,0.002982513,0.007584987],"category_scores_gemma":[0.08782364,0.001788006,0.002621629,0.003091931,0.00270232,0.004965476,0.006605858,0.006775503,0.001924482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002310824,"about_ca_system_score_gemma":0.002772777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002421592,"about_ca_topic_score_gemma":0.00331239,"domain_scores_codex":[0.9838307,0.01051644,0.0008834593,0.001587157,0.002790213,0.0003919595],"domain_scores_gemma":[0.9308736,0.06059137,0.001652525,0.003471504,0.002763649,0.0006472826],"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":[0.0003663452,0.0001546364,0.001576822,0.0006911224,0.0001539381,0.0003526122,0.00172979,0.2045656,0.004571921,0.5918662,0.006233237,0.1877377],"study_design_scores_gemma":[0.0000403604,0.00003644715,0.0002383946,0.0001408367,0.00002934675,0.0001075333,0.0001665131,0.4094383,0.002037404,0.5823117,0.005387688,0.00006546068],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009776051,0.00005853256,0.9979303,0.0001468008,0.0000160456,0.00007127364,0.00006535486,0.00008978883,0.0006443723],"genre_scores_gemma":[0.06518193,0.0003109069,0.931107,0.0001897206,0.0001264734,0.0007867705,0.0004644789,0.0001170209,0.001715726],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01891317,"threshold_uncertainty_score":0.1000236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02501472575385925,"score_gpt":0.3222581612052189,"score_spread":0.2972434354513597,"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."}}