{"id":"W1439202096","doi":"10.1515/mcma-2013-0019","title":"Rare event simulation for diffusion processes via two-stage importance sampling","year":2014,"lang":"en","type":"article","venue":"Monte Carlo Methods and Applications","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Estimator; Brownian motion; Rare events; Importance sampling; Mathematics; Measure (data warehouse); Entropy (arrow of time); Applied mathematics; Event (particle physics); Sampling (signal processing); Large deviations theory; Statistical physics; Path (computing); Computer science; Mathematical optimization; Monte Carlo method; Statistics; Data mining; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.009032401,0.0006373625,0.001326368,0.001328342,0.0004045307,0.001133576,0.001471548,0.001339316,0.001233997],"category_scores_gemma":[0.02742331,0.0007321782,0.001010941,0.0006725403,0.001878378,0.002007729,0.001762068,0.001630379,0.0001378059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312474,"about_ca_system_score_gemma":0.0008920425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002997445,"about_ca_topic_score_gemma":0.002473648,"domain_scores_codex":[0.9975863,0.001586859,0.00008444469,0.0002181669,0.0003893119,0.0001349298],"domain_scores_gemma":[0.9766803,0.01998299,0.001052251,0.0009539716,0.0008736376,0.0004566613],"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":[0.0001711525,0.00005888661,0.002587521,0.00006378593,0.00005680119,0.0001117899,0.00009146983,0.9181016,0.001587302,0.06870938,0.0001391167,0.008321072],"study_design_scores_gemma":[0.00001017302,0.00001294989,0.0001356345,0.00000275891,0.000002829612,0.000008396738,0.000002219554,0.9937299,0.0002859046,0.005758616,0.00004599385,0.000004581482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03932986,0.00009838326,0.9598901,0.0001182564,0.00001581339,0.00005691592,0.00001769211,0.00007883325,0.000394121],"genre_scores_gemma":[0.7804539,0.0001390239,0.2178221,0.0000808968,0.00004346386,0.0002047754,0.000105269,0.00004024656,0.001110418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009032401,"threshold_uncertainty_score":0.04776847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1763937691469354,"score_gpt":0.5054004319798635,"score_spread":0.3290066628329281,"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."}}