{"id":"W3184008167","doi":"10.1109/wsc52266.2021.9715488","title":"Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation","year":2021,"lang":"en","type":"article","venue":"2021 Winter Simulation Conference (WSC)","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Variance reduction; Quantile; Monte Carlo method; Estimator; Control variates; Statistics; Mathematics; Variance (accounting); Likelihood-ratio test; Reduction (mathematics); Sensitivity (control systems); Restricted maximum likelihood; Applied mathematics; Computer science; Estimation theory; Hybrid Monte Carlo; Markov chain Monte Carlo; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001124462,0.0002423727,0.0004410319,0.0001400165,0.0001100326,0.0001983714,0.00008333897,0.000180444,0.001266353],"category_scores_gemma":[0.002438215,0.0002402102,0.0001473126,0.0003630842,0.00002918017,0.0005881538,0.00003933857,0.0001864539,0.00005072279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001232118,"about_ca_system_score_gemma":0.0001757909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000216885,"about_ca_topic_score_gemma":0.00007646383,"domain_scores_codex":[0.9976395,0.0004816786,0.0007990594,0.0005041756,0.000305014,0.000270543],"domain_scores_gemma":[0.9974215,0.0009799909,0.0002954262,0.0003956528,0.0008290788,0.00007835811],"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":[0.0001559387,0.0006078699,0.00002906101,0.0004218593,0.0000690712,0.000007172604,0.003292936,0.03977492,0.02429015,0.8944309,0.001130031,0.03579012],"study_design_scores_gemma":[0.0007345481,0.00003075863,0.00005944207,0.0001325761,0.00003768381,0.000009378121,0.0003068917,0.7476858,0.02397344,0.2265145,0.000319761,0.0001951206],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02250838,0.000009049021,0.9735493,0.001412915,0.0003290174,0.0007547471,0.00002858852,0.00008265949,0.001325314],"genre_scores_gemma":[0.5584289,0.000002877755,0.4398723,0.00008546984,0.0001164308,0.0001048178,0.0001701565,0.00002198839,0.001197034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.707911,"threshold_uncertainty_score":0.9996466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09300308759556601,"score_gpt":0.3917001228777633,"score_spread":0.2986970352821973,"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."}}