{"id":"W2804233805","doi":"10.48550/arxiv.1709.06181","title":"On Nesting Monte Carlo Estimators","year":2017,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Air Force Research Laboratory; Institute for Information and Communications Technology Promotion; Defense Advanced Research Projects Agency; Ministry of Science, ICT and Future Planning; European Commission; University of Oxford; Nvidia","keywords":"Estimator; Nesting (process); Monte Carlo method; Convergence (economics); Computer science; Mathematical optimization; Rate of convergence; Applied mathematics; Bayesian probability; Mathematics; Econometrics; Statistics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001456795,0.0001197689,0.0001120247,0.0000756153,0.0007310174,0.0002114267,0.00136487,0.00004361194,0.000009228179],"category_scores_gemma":[0.0001578216,0.0001235032,0.00006469073,0.0001243301,0.00006329286,0.0004715376,0.0003297668,0.0001970455,0.0001489069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003316353,"about_ca_system_score_gemma":0.00002927449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003501539,"about_ca_topic_score_gemma":0.0000127505,"domain_scores_codex":[0.9991699,0.00004166233,0.00006452677,0.0004341672,0.00005892586,0.0002308106],"domain_scores_gemma":[0.9986606,0.00008084148,0.0001268649,0.0009769711,0.00003861924,0.0001161515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001414542,0.00007252498,0.05178331,0.00001302484,0.00002786752,0.001043189,0.0002507468,0.3418719,0.00002375397,0.5860317,0.0005356563,0.01833208],"study_design_scores_gemma":[0.0003059485,0.00005860705,0.01346633,0.00002644517,0.000006261407,0.000006000912,0.00001037797,0.9796704,0.00003655042,0.005810137,0.0004328353,0.0001701189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.728773,0.000005784063,0.257559,0.0002688948,0.000385589,0.00005200762,7.425611e-7,0.0002463879,0.01270865],"genre_scores_gemma":[0.9942097,0.000002689362,0.002310595,0.00007846148,0.00004731402,1.041655e-7,1.956402e-7,0.000007005784,0.003343917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6377984,"threshold_uncertainty_score":0.5622469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05383123295369001,"score_gpt":0.19286674453687,"score_spread":0.13903551158318,"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."}}