{"id":"W2609475175","doi":"10.5555/3320516.3320717","title":"Uniform convergence of sample average approximation with adaptive multiple importance sampling","year":2018,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Convergence (economics); Sample (material); Sample mean and sample covariance; Sample size determination; Mathematics; Context (archaeology); Sampling (signal processing); Stochastic approximation; Applied mathematics; Mathematical optimization; Law of large numbers; Uniform convergence; Function (biology); Statistics; Computer science; Random variable; Bandwidth (computing); Physics","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":[0.01652122,0.001709293,0.002342522,0.002061167,0.0006195852,0.001847688,0.002891189,0.001803081,0.001664513],"category_scores_gemma":[0.08958255,0.0008827611,0.001578005,0.001710235,0.00281991,0.004064843,0.003091242,0.003068107,0.0002998832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002053542,"about_ca_system_score_gemma":0.00158053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004705037,"about_ca_topic_score_gemma":0.002208813,"domain_scores_codex":[0.9936934,0.003664633,0.0002460949,0.0006333066,0.001377192,0.0003855223],"domain_scores_gemma":[0.950488,0.04112551,0.001994243,0.002408774,0.00342347,0.0005600655],"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":[0.0002050617,0.00009101589,0.001998682,0.0002702523,0.0001673997,0.0001445494,0.0001598313,0.7879934,0.00175226,0.1824182,0.001122498,0.02367678],"study_design_scores_gemma":[0.000009401744,0.00002193938,0.00006988725,0.000009954751,0.00000825244,0.0000128937,0.000005731374,0.9852988,0.0002824378,0.01413871,0.00013729,0.000004694006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006864512,0.000335002,0.9916351,0.0001238555,0.0000408513,0.00003716801,0.00001648663,0.00007125273,0.0008758574],"genre_scores_gemma":[0.615314,0.001540053,0.3783861,0.0003353397,0.0003336101,0.0005205342,0.0002400005,0.0002233162,0.003106985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01652122,"threshold_uncertainty_score":0.08737367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2175555294383031,"score_gpt":0.3771516586903245,"score_spread":0.1595961292520214,"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."}}