{"id":"W3094638502","doi":"10.1002/9781118445112.stat08240","title":"Randomized Quasi‐Monte Carlo","year":2020,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Monte Carlo method; Markov chain Monte Carlo; Hybrid Monte Carlo; Monte Carlo integration; Mathematics; Quasi-Monte Carlo method; Estimator; Monte Carlo method in statistical physics; Dynamic Monte Carlo method; Monte Carlo molecular modeling; Applied mathematics; Statistical physics; Variance reduction; Random variable; Mathematical optimization; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004668229,0.0008300109,0.002233939,0.0002840662,0.00007978567,0.000128589,0.0005323095,0.0005218512,0.01436774],"category_scores_gemma":[0.004255694,0.0006364239,0.0002165862,0.0002740792,0.0003399978,0.00006904973,0.000126861,0.000859937,0.0006530533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000966844,"about_ca_system_score_gemma":0.0002325644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002232627,"about_ca_topic_score_gemma":0.0008435571,"domain_scores_codex":[0.995887,0.0004575096,0.001434325,0.0007394367,0.0009790852,0.0005026649],"domain_scores_gemma":[0.9960349,0.001413978,0.001174817,0.0007583376,0.0002521089,0.0003658394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007847743,0.000287579,2.829811e-7,0.0006991737,0.0001580632,0.00001358202,0.0001895241,1.805909e-7,0.000002225914,0.5044809,0.4911387,0.002245019],"study_design_scores_gemma":[0.03098349,0.0001895899,4.40749e-7,0.001350313,0.0005504417,0.000007608867,0.0002087815,0.02254983,0.000009308826,0.5038612,0.4392673,0.001021642],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001302205,0.0004772772,0.6920719,0.0005904674,0.0004217602,0.002406899,0.02074428,0.000996155,0.2822782],"genre_scores_gemma":[0.00005984405,0.001381693,0.6566678,0.0003589894,0.0003698631,0.0001552267,0.002704359,0.00067659,0.3376256],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05534738,"threshold_uncertainty_score":0.9996087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1004754910846677,"score_gpt":0.3651792866733858,"score_spread":0.2647037955887181,"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."}}