{"id":"W2163956611","doi":"10.1007/s10260-015-0299-6","title":"Blending Bayesian and frequentist methods according to the precision of prior information with applications to hypothesis testing","year":2015,"lang":"en","type":"article","venue":"Statistical Methods & Applications","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Statistics Canada; University of Ottawa","funders":"University of Ottawa","keywords":"Frequentist inference; Posterior probability; Prior probability; Bayesian probability; Minimax; p-value; Mathematics; Statistics; Statistical hypothesis testing; Inference; Null hypothesis; Point estimation; Bayesian inference; Prior information; Value (mathematics); Computer science; Artificial intelligence; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003971844,0.0002790569,0.000523607,0.0002064534,0.0003850396,0.0001098374,0.0003880691,0.00008677178,0.0000141929],"category_scores_gemma":[0.01846443,0.000200526,0.00003645948,0.001158986,0.0001813771,0.0002377458,0.0002305355,0.0002483142,0.00001596551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001040904,"about_ca_system_score_gemma":0.0001199231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004655445,"about_ca_topic_score_gemma":0.00001277765,"domain_scores_codex":[0.9970462,0.0007892296,0.0008777507,0.000484781,0.0003953298,0.0004066777],"domain_scores_gemma":[0.9730477,0.02469959,0.0003192852,0.0007650417,0.000595198,0.0005732253],"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":[0.0000288914,0.00003535912,0.00002667016,0.00006579236,0.00001816736,1.778076e-7,0.0003201948,0.0001031069,0.0009162096,0.3690296,0.00008972917,0.6293661],"study_design_scores_gemma":[0.0003009134,0.0002122936,0.0003590515,0.00007996016,0.0002070975,0.00002067783,0.001255794,0.006887573,0.001866867,0.9708322,0.01764515,0.0003323761],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00006750229,0.00003289337,0.9935699,0.0005552399,0.00003255951,0.002968708,0.0003519085,0.0001005676,0.002320759],"genre_scores_gemma":[0.004477778,0.000004156082,0.9918947,0.0002006629,0.00006900133,0.003265601,0.00001010776,0.00004460228,0.00003333816],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6290337,"threshold_uncertainty_score":0.9898034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2059212699054185,"score_gpt":0.5003699745578156,"score_spread":0.2944487046523971,"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."}}