{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02117007,0.001511231,0.002568447,0.001995421,0.0008326691,0.003247091,0.002713782,0.002929948,0.002999961],"category_scores_gemma":[0.08190772,0.001260632,0.001045719,0.001698848,0.004307371,0.006516091,0.003381632,0.00373182,0.0006230797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00279053,"about_ca_system_score_gemma":0.001991786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001088913,"about_ca_topic_score_gemma":0.0007769665,"domain_scores_codex":[0.9919289,0.005466308,0.0002310744,0.00097911,0.001090159,0.0003044402],"domain_scores_gemma":[0.9475186,0.04637375,0.00239526,0.001907983,0.001412228,0.0003921318],"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":[0.0001271935,0.000104543,0.001851383,0.0002424924,0.0001476084,0.00008813646,0.0002105818,0.3501391,0.0006190465,0.6054892,0.002651256,0.03832949],"study_design_scores_gemma":[0.00002823253,0.0000713768,0.000491239,0.00009521904,0.00001947808,0.00006613014,0.00003322753,0.6567744,0.0006067656,0.340249,0.001534392,0.00003062527],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01302349,0.0009506054,0.9820511,0.000991824,0.00003959188,0.00005019884,0.00009222419,0.0001233385,0.002677635],"genre_scores_gemma":[0.5133112,0.002958194,0.468321,0.0008297582,0.0005766286,0.001203725,0.0006818057,0.0004013208,0.01171633],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02117007,"threshold_uncertainty_score":0.1119594,"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."}}