{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002447281,0.00006406526,0.000145177,0.0001166345,0.0001609558,0.000109597,0.0002637774,0.00005526223,0.0001794397],"category_scores_gemma":[0.002604456,0.0000417225,0.00009761117,0.0007626672,0.00002534991,0.0002377696,0.00004771258,0.00003088815,0.0002514786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001415568,"about_ca_system_score_gemma":0.00003318863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001175065,"about_ca_topic_score_gemma":0.00006718947,"domain_scores_codex":[0.9985844,0.0000442282,0.0004212481,0.0003283706,0.0004361637,0.0001855742],"domain_scores_gemma":[0.9972458,0.002023157,0.00008633079,0.000366393,0.0002257408,0.00005250811],"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":[0.00004821766,0.00001598355,0.01029911,0.000004116006,0.000005274687,5.239273e-7,0.0002538903,0.9241273,0.0007138632,0.03526405,0.004039547,0.02522809],"study_design_scores_gemma":[0.0001862371,0.000009211251,0.004824058,0.000001104136,0.000001681026,1.393943e-7,0.00004552192,0.6243862,0.0001031339,0.3589611,0.01142993,0.00005171101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5564333,0.00001643029,0.4415832,0.0008522735,0.0001890435,0.000262263,0.000008713495,0.0001637516,0.0004909676],"genre_scores_gemma":[0.9776015,0.000004587599,0.01666242,0.0002982724,0.00007810192,0.00002822171,0.00001494314,0.000006630172,0.005305321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4249208,"threshold_uncertainty_score":0.3232333,"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."}}