{"id":"W3143454015","doi":"10.3390/stats4020018","title":"Measuring Bayesian Robustness Using Rényi Divergence","year":2021,"lang":"en","type":"article","venue":"Stats","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Prior probability; Robustness (evolution); Bayesian probability; Curvature; Computer science; Mathematics; Divergence (linguistics); Artificial intelligence; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01418677,0.001588122,0.002078023,0.006762076,0.0008709577,0.003702773,0.001950286,0.002469486,0.001740091],"category_scores_gemma":[0.07667135,0.0006690648,0.001799919,0.002791983,0.003702214,0.005474114,0.004708346,0.00250195,0.0004865329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002136108,"about_ca_system_score_gemma":0.0008745456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001287988,"about_ca_topic_score_gemma":0.0005491622,"domain_scores_codex":[0.9875746,0.004903514,0.0008022766,0.001941458,0.004252145,0.0005260224],"domain_scores_gemma":[0.943181,0.0400278,0.00720348,0.005267706,0.003166762,0.001153331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007824377,0.0001244685,0.01855119,0.0003549771,0.000793829,0.0003568183,0.0005215525,0.7310277,0.0130574,0.1409181,0.0008445807,0.09266696],"study_design_scores_gemma":[0.00002621582,0.0002489891,0.01171412,0.00009625786,0.00009819664,0.0004245642,0.0001276005,0.8012254,0.009462704,0.1746809,0.00164311,0.0002518993],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1002247,0.0007561335,0.8946598,0.0002306201,0.00003863658,0.00006625064,0.0002110919,0.0004046294,0.003408252],"genre_scores_gemma":[0.9038215,0.0006310668,0.09340136,0.0001656507,0.0001305809,0.0001324541,0.0007082389,0.0002241673,0.0007849702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01418677,"threshold_uncertainty_score":0.0750277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3417578879156906,"score_gpt":0.4389721859944113,"score_spread":0.09721429807872078,"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."}}