{"id":"W1987069098","doi":"10.5555/1030818.1030863","title":"New simulation methodology for finance: efficient simulation of gamma and variance-gamma processes","year":2003,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Monte Carlo method; Rejection sampling; Brownian bridge; Monte Carlo integration; Importance sampling; Hybrid Monte Carlo; Quasi-Monte Carlo method; Gamma process; Variance (accounting); Variance-gamma distribution; Computer science; Stochastic process; Variance reduction; Sampling (signal processing); Mathematics; Algorithm; Statistical physics; Brownian motion; Markov chain Monte Carlo; Statistics; Physics; Accounting; Estimator","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":[0.0007321765,0.0002258862,0.0004142517,0.0001357716,0.00008882556,0.00006001104,0.0001099499,0.0001827196,0.0003672618],"category_scores_gemma":[0.007636311,0.0001995799,0.00007372758,0.0002469341,0.00006003886,0.0002313965,0.00002177492,0.0001063986,0.000007201041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003519181,"about_ca_system_score_gemma":0.0001463694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003206394,"about_ca_topic_score_gemma":0.000008575304,"domain_scores_codex":[0.9982028,0.0002015208,0.0007727547,0.0003607842,0.000255075,0.0002070422],"domain_scores_gemma":[0.9934174,0.0048359,0.0004806923,0.000264307,0.0009314516,0.00007021801],"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":[0.0001165247,0.0001013321,0.00004800297,0.0005516942,0.00002239073,1.280773e-7,0.002311865,0.6115822,0.0002933111,0.3774467,0.00004329755,0.007482533],"study_design_scores_gemma":[0.0007275895,0.0001040773,0.00006967849,0.000128165,0.00003890189,8.42177e-7,0.0001135963,0.7485684,0.003339542,0.2456807,0.001071911,0.0001565573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04335793,0.00003169196,0.9539994,0.0001027305,0.0001243731,0.0009395438,0.00001141085,0.00006602149,0.001366883],"genre_scores_gemma":[0.7743261,0.000002477906,0.2248705,0.00003759588,0.00004204948,0.00003097471,0.00001303767,0.00001850277,0.0006588149],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7309682,"threshold_uncertainty_score":0.9141929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2067321173081588,"score_gpt":0.4051801057558147,"score_spread":0.1984479884476559,"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."}}