{"id":"W2150507062","doi":"10.5555/1161734.1162022","title":"Randomized Quasi-Monte Carlo: a tool for improving the efficiency of simulations in finance","year":2004,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Monte Carlo method; Estimator; Computer science; Robustness (evolution); Constructive; Quasi-Monte Carlo method; Mathematical optimization; Econometrics; Finance; Hybrid Monte Carlo; Mathematics; Markov chain Monte Carlo; Statistics; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000782842,0.0001444906,0.0003945037,0.00009392542,0.00007397209,0.00004787878,0.0001850134,0.00007085141,0.0000683691],"category_scores_gemma":[0.003674859,0.00009590596,0.0001472738,0.000192584,0.0001094693,0.0002017866,0.00002697864,0.0001065986,0.000004186777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005280188,"about_ca_system_score_gemma":0.00008168549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002929935,"about_ca_topic_score_gemma":0.00008679722,"domain_scores_codex":[0.9985206,0.0001012056,0.000811093,0.0001984559,0.0002106369,0.0001580218],"domain_scores_gemma":[0.9966962,0.002296274,0.0003356149,0.000287212,0.0003671652,0.00001747302],"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":[0.001807431,0.0003188164,0.00002964818,0.0002211363,0.00001573717,1.841983e-7,0.006142627,0.08041763,0.0003813514,0.9091058,0.000008754873,0.001550927],"study_design_scores_gemma":[0.01740726,0.00004166274,0.00002261115,0.0001189535,0.00002134094,3.211352e-7,0.0001101448,0.745764,0.001015147,0.2353868,0.00002090912,0.0000909412],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.354203,0.000008389999,0.643894,0.0003354923,0.00004981501,0.001197264,0.00001015048,0.00002500122,0.000276993],"genre_scores_gemma":[0.9784726,9.348776e-7,0.02108806,0.00006129927,0.00002319105,0.0001279781,0.000004613556,0.00001188857,0.0002094244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.673719,"threshold_uncertainty_score":0.4399415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06034456591272242,"score_gpt":0.3371943436228804,"score_spread":0.276849777710158,"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."}}