{"id":"W4391409082","doi":"10.1109/wsc60868.2023.10408062","title":"Generalized Importance Sampling for Nested Simulation","year":2023,"lang":"en","type":"article","venue":"","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sampling (signal processing); Estimator; Computer science; Variance reduction; Variance (accounting); Importance sampling; Computation; Set (abstract data type); Mathematical optimization; Nested set model; Algorithm; Mathematics; Data mining; Statistics; Monte Carlo method","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007477378,0.001001521,0.001603178,0.001031476,0.0006355125,0.001028176,0.001703578,0.0007794998,0.002107964],"category_scores_gemma":[0.02300626,0.0006765295,0.001513645,0.0009873741,0.001423745,0.001354538,0.002160529,0.001750822,0.0002897735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00112215,"about_ca_system_score_gemma":0.001654307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004450855,"about_ca_topic_score_gemma":0.003214959,"domain_scores_codex":[0.9943765,0.003785506,0.0001636176,0.000489827,0.0009661127,0.000218536],"domain_scores_gemma":[0.9876314,0.009153297,0.0005346558,0.001298154,0.001057184,0.0003253902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001301812,0.00007228662,0.002008149,0.0001574307,0.0001464473,0.0001535197,0.0001324891,0.7831396,0.001871177,0.1631866,0.0009439769,0.04805813],"study_design_scores_gemma":[0.00001233168,0.00002150918,0.0001131312,0.000007001488,0.000009799479,0.00001645894,0.000004196138,0.9700182,0.0002604396,0.02900351,0.0005278161,0.000005585076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002337492,0.00006636355,0.9971323,0.00003073427,0.00001482144,0.00003514029,0.00001527286,0.00007282092,0.0002950739],"genre_scores_gemma":[0.313421,0.0003794699,0.6836504,0.0001531086,0.0001215551,0.0005499216,0.0002812362,0.0001290932,0.001314137],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007477378,"threshold_uncertainty_score":0.03954458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4974642677841926,"score_gpt":0.5072837087062296,"score_spread":0.00981944092203707,"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."}}