{"id":"W1990674830","doi":"10.1145/1225275.1225280","title":"Rare events, splitting, and quasi-Monte Carlo","year":2007,"lang":"en","type":"article","venue":"ACM Transactions on Modeling and Computer Simulation","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Estimator; Monte Carlo method; Variance reduction; Importance sampling; Computer science; Rare events; Context (archaeology); Markov chain Monte Carlo; Disjoint sets; Markov chain; Algorithm; Sequence (biology); Measure (data warehouse); Sampling (signal processing); Mathematical optimization; Statistical physics; Mathematics; Statistics; Discrete mathematics; Physics; Machine learning","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":[0.009459384,0.0008524217,0.001365651,0.00130921,0.0008118094,0.00169731,0.002435366,0.001705672,0.003203861],"category_scores_gemma":[0.03981859,0.0008527995,0.001347023,0.001217682,0.00312308,0.003343282,0.002270481,0.002533064,0.0005721928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001298158,"about_ca_system_score_gemma":0.00164253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004213551,"about_ca_topic_score_gemma":0.003113979,"domain_scores_codex":[0.9922612,0.004902125,0.0002661092,0.00091048,0.001345786,0.0003143472],"domain_scores_gemma":[0.9623275,0.02846739,0.002006607,0.005065562,0.001509652,0.0006231467],"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":[0.0001978667,0.00007789155,0.002760207,0.0002791059,0.0001448821,0.0001774846,0.0002690935,0.4913656,0.001444625,0.4611167,0.001547748,0.0406187],"study_design_scores_gemma":[0.00002538195,0.00005752597,0.0002956351,0.00003898364,0.00002483475,0.00006086132,0.00001851374,0.870205,0.0008186334,0.1262481,0.002180154,0.00002636151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006087228,0.0004955544,0.9914135,0.0002253563,0.00007431548,0.00005020607,0.00004637523,0.0002815727,0.001325973],"genre_scores_gemma":[0.3849614,0.001083437,0.6097718,0.0004665601,0.0002389126,0.0003593477,0.0003142743,0.0003300089,0.002474208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009459384,"threshold_uncertainty_score":0.05002666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1006733848618893,"score_gpt":0.3640801606970366,"score_spread":0.2634067758351473,"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."}}