{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04586072,0.001559278,0.002307252,0.002117389,0.00139207,0.002940188,0.003773612,0.003479753,0.004864427],"category_scores_gemma":[0.2218518,0.001679117,0.001680194,0.001852397,0.005439365,0.007228321,0.005273842,0.006582135,0.001074281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002320187,"about_ca_system_score_gemma":0.002336886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003204991,"about_ca_topic_score_gemma":0.002964531,"domain_scores_codex":[0.9731683,0.02058129,0.0008065354,0.001535504,0.003308079,0.0006003657],"domain_scores_gemma":[0.8321945,0.1440201,0.00511437,0.01182313,0.005640884,0.001206976],"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":[0.00008101566,0.00006284465,0.001857552,0.0001692904,0.00007917683,0.0001179164,0.0003611913,0.137411,0.000839896,0.8245426,0.001290835,0.03318667],"study_design_scores_gemma":[0.00003023733,0.00005116318,0.0002923917,0.0001019503,0.00002258794,0.00007162157,0.00002561514,0.5959155,0.0005200431,0.4008033,0.002143814,0.0000216703],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001617313,0.0001578761,0.9969886,0.0001426885,0.00001927453,0.00003800757,0.00001593702,0.00007263551,0.0009477249],"genre_scores_gemma":[0.1536493,0.0005402999,0.8412544,0.0006048957,0.0001898258,0.0007051989,0.0001749342,0.0003167643,0.002564514],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04586072,"threshold_uncertainty_score":0.2425376,"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."}}