{"id":"W2128699672","doi":"10.5555/1218112.1218142","title":"Splitting for rare-event simulation","year":2006,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Rare events; Estimator; Disjoint sets; Monte Carlo method; Sampling (signal processing); Importance sampling; Variance (accounting); Computer science; Event (particle physics); State space; Rejection sampling; Algorithm; Statistics; Theoretical computer science; Mathematical optimization; Mathematics; Hybrid Monte Carlo; Discrete mathematics; Markov chain Monte Carlo; Telecommunications; 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":[],"consensus_categories":[],"category_scores_codex":[0.001569928,0.0001500618,0.0002329851,0.0001515111,0.0002086211,0.0003398031,0.0004024583,0.0001028068,0.0003704192],"category_scores_gemma":[0.001562714,0.000118459,0.0001655914,0.0002512268,0.000066202,0.0005952182,0.00008003508,0.00008782141,0.0001459213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005073348,"about_ca_system_score_gemma":0.0000662579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002647532,"about_ca_topic_score_gemma":0.00005906455,"domain_scores_codex":[0.9975827,0.0001320586,0.0008427016,0.0005428614,0.000652279,0.0002473649],"domain_scores_gemma":[0.9955036,0.002732272,0.0002963631,0.0004834956,0.0009249512,0.00005928775],"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":[0.0000524668,0.00004102039,0.003381777,0.000004882958,0.000003334756,3.036443e-7,0.0002416668,0.9552448,0.0001073766,0.01202862,0.0001826197,0.02871112],"study_design_scores_gemma":[0.0003316021,0.00003188785,0.006035499,0.00001618224,0.000005727674,2.620765e-7,0.00004510797,0.7614547,0.00018816,0.2203663,0.01140535,0.0001191631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.153649,0.00001966366,0.8426512,0.0005496175,0.0002971483,0.0004229395,0.00002083466,0.00006784749,0.002321715],"genre_scores_gemma":[0.9935057,2.949025e-7,0.003343446,0.0001402546,0.000225876,0.00002006558,0.00002300726,0.000009928065,0.002731497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8398566,"threshold_uncertainty_score":0.4830619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1939460541183561,"score_gpt":0.4327162746856458,"score_spread":0.2387702205672897,"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."}}