{"id":"W2475685041","doi":"10.1007/978-3-642-59657-5_22","title":"A Comparison of Monte Carlo, Lattice Rules and Other Low-Discrepancy Point Sets","year":2000,"lang":"en","type":"book-chapter","venue":"","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Monte Carlo method; Variance reduction; Quasi-Monte Carlo method; Control variates; Hybrid Monte Carlo; Estimator; Curse of dimensionality; Statistical physics; Monte Carlo integration; Lattice (music); Monte Carlo molecular modeling; Monte Carlo method in statistical physics; Computer science; Mathematics; Econometrics; 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":[0.00732465,0.0005713873,0.002224188,0.003480704,0.001432592,0.004822085,0.003150612,0.001718033,0.007685687],"category_scores_gemma":[0.02754613,0.0003399673,0.000827361,0.004456291,0.00240579,0.004767334,0.002164206,0.002003425,0.001323123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001669846,"about_ca_system_score_gemma":0.001179358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001488132,"about_ca_topic_score_gemma":0.001840784,"domain_scores_codex":[0.9950911,0.001830194,0.0001387761,0.0001769892,0.002623655,0.000139332],"domain_scores_gemma":[0.9831671,0.01259727,0.0004006024,0.002175209,0.0013854,0.000274406],"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":[0.0001045692,0.00005824069,0.0003270749,0.000166149,0.00003077892,0.00001751427,0.00008546732,0.03266227,0.0001862572,0.9256632,0.003197084,0.03750134],"study_design_scores_gemma":[0.00003483499,0.00004138908,0.0003833667,0.00008933341,0.00002353798,0.0000854981,0.00008591455,0.2189967,0.0007988616,0.7712349,0.008203197,0.00002248028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07452929,0.01948364,0.6457193,0.002516543,0.0008463033,0.0001634772,0.0006547744,0.001142196,0.2549446],"genre_scores_gemma":[0.6372986,0.01245954,0.3206603,0.0006136668,0.0005172252,0.0002939372,0.001215771,0.001426138,0.02551483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007685687,"threshold_uncertainty_score":0.03873694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0750372470596037,"score_gpt":0.3458256693937722,"score_spread":0.2707884223341685,"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."}}