{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006159197,0.0009195791,0.001189367,0.00115453,0.0005184003,0.001446318,0.002062234,0.001681424,0.002202031],"category_scores_gemma":[0.02265067,0.0006701487,0.0009597712,0.001571951,0.001749755,0.003055822,0.001590422,0.002085537,0.0004590518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001243987,"about_ca_system_score_gemma":0.001297923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002234993,"about_ca_topic_score_gemma":0.00163646,"domain_scores_codex":[0.9977157,0.001452038,0.00007865595,0.0001525561,0.000514787,0.00008630738],"domain_scores_gemma":[0.9924191,0.005567863,0.0003770835,0.0006436559,0.0008027061,0.000189673],"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":[0.00003787933,0.00004234732,0.000732505,0.00007362635,0.00006323594,0.0000570547,0.0000952228,0.5119065,0.0009374602,0.4607848,0.000943428,0.02432594],"study_design_scores_gemma":[0.00001457006,0.00001207988,0.00005626162,0.000009491442,0.000006545888,0.00002472339,0.000004558532,0.9204026,0.0002421983,0.07796205,0.001257634,0.000007302459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001855377,0.0001663626,0.9973168,0.00009122934,0.00002393432,0.00001868467,0.000008986383,0.00005376524,0.0004648824],"genre_scores_gemma":[0.1348691,0.0008970571,0.8615139,0.0001693764,0.0001360643,0.0003977806,0.0000957942,0.0001481388,0.001772766],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006159197,"threshold_uncertainty_score":0.03257334,"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."}}