{"id":"W4390048227","doi":"10.48550/arxiv.2312.12149","title":"Bayesian and minimax estimators of loss","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Estimator; Mathematics; Minimax estimator; Poisson distribution; Bayes estimator; Univariate; Prior probability; Bayesian probability; Sigma; Applied mathematics; Combinatorics; Statistics; Multivariate statistics; Minimum-variance unbiased estimator; Mathematical optimization; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0001204186,0.0001650507,0.0002791978,0.000111094,0.0000720385,0.00001733205,0.0002192156,0.0001682175,0.0001177374],"category_scores_gemma":[0.0003745548,0.0001914418,0.00008244583,0.0002725816,0.0002368373,0.00003810376,0.0003157548,0.0002004583,0.00005181775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005145322,"about_ca_system_score_gemma":0.00006641538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003569285,"about_ca_topic_score_gemma":0.00001200295,"domain_scores_codex":[0.9991142,0.00004140495,0.0002333034,0.0004008981,0.00006301556,0.0001471607],"domain_scores_gemma":[0.9986308,0.0004865617,0.0002192323,0.000415122,0.0001222791,0.0001260299],"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":[0.000009065755,0.00006616826,0.001372087,0.0002851012,0.00004467935,0.00002452087,0.00005031224,0.001437681,0.00000593736,0.995057,0.001550223,0.00009716812],"study_design_scores_gemma":[0.0002351634,0.00001208083,0.004785588,0.00009707302,0.0001367937,0.00000200542,0.00009103604,0.1913914,0.0000529568,0.8029258,0.00008041928,0.0001896619],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1165778,0.000005834348,0.8812613,0.0001372999,0.0000675321,0.0002358586,0.0005768103,0.0001617352,0.0009757644],"genre_scores_gemma":[0.992812,0.00003363119,0.006037366,0.0000116916,0.00001359854,0.000002243786,0.00009034982,0.00001997158,0.000979082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8762343,"threshold_uncertainty_score":0.7806771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2058847689629625,"score_gpt":0.2754454440488968,"score_spread":0.06956067508593425,"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."}}