{"id":"W2081241186","doi":"10.5555/510378.510456","title":"Quasi-random numbers and their applications: using lattice rules for variance reduction in simulation","year":2000,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Calgary","funders":"","keywords":"Monte Carlo method; Variance reduction; Quasi-Monte Carlo method; Monte Carlo integration; Control variates; Hybrid Monte Carlo; Monte Carlo molecular modeling; Estimator; Monte Carlo method in statistical physics; Lattice (music); Computer science; Dynamic Monte Carlo method; Statistical physics; Mathematical optimization; Mathematics; Algorithm; Applied mathematics; Markov chain Monte Carlo; Statistics; 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":[0.01163444,0.000681688,0.001402012,0.00182216,0.0006908077,0.002568912,0.001793033,0.001810644,0.002357293],"category_scores_gemma":[0.04515231,0.0007492611,0.001028556,0.001548678,0.004082713,0.003017686,0.002377813,0.002791362,0.0006168896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001307861,"about_ca_system_score_gemma":0.001508076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002131029,"about_ca_topic_score_gemma":0.001537357,"domain_scores_codex":[0.9890938,0.00811312,0.0002683563,0.0003579371,0.001954978,0.0002119422],"domain_scores_gemma":[0.975246,0.0201294,0.0009509228,0.002030483,0.00132357,0.0003196312],"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":[0.00002723165,0.00002773408,0.0003674637,0.00008883789,0.00003014206,0.00003988113,0.00007023186,0.2072846,0.0004910496,0.7758895,0.0005598414,0.01512343],"study_design_scores_gemma":[0.00001504614,0.00001950292,0.00009577879,0.00003011822,0.000004574805,0.00002362663,0.00001113255,0.7429302,0.000275276,0.2549276,0.001649835,0.00001727932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003889416,0.00103901,0.9910939,0.0004128231,0.0001041524,0.00003868168,0.00001520396,0.000127765,0.003279],"genre_scores_gemma":[0.2565235,0.002301552,0.7376468,0.0003516012,0.0003098044,0.0003549218,0.00006470523,0.0003213551,0.002125752],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01163444,"threshold_uncertainty_score":0.06152952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09636242107222065,"score_gpt":0.3637437136395786,"score_spread":0.267381292567358,"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."}}