{"id":"W2063877455","doi":"10.1504/ijhpcn.2006.013487","title":"A parallel quasi-Monte Carlo approach to pricing multidimensional American options","year":2006,"lang":"en","type":"article","venue":"International Journal of High Performance Computing and Networking","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Computer science; Speedup; Scalability; Monte Carlo method; Parallel computing; Monte Carlo methods for option pricing; Recursion (computer science); Parallel algorithm; Mathematical optimization; Quasi-Monte Carlo method; Markov chain Monte Carlo; Algorithm; Hybrid Monte Carlo; Mathematics; Artificial intelligence","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":[0.0006227313,0.0001437708,0.0002834251,0.000198705,0.0001447364,0.00008630046,0.000190492,0.00003433815,0.000004669429],"category_scores_gemma":[0.00005940393,0.000114862,0.00007234066,0.0001644769,0.0000505444,0.0001632991,0.00007482751,0.0002261485,0.000002720487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007803173,"about_ca_system_score_gemma":0.00002549034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005230153,"about_ca_topic_score_gemma":0.00000522929,"domain_scores_codex":[0.9984511,0.00005028229,0.0006776957,0.0001466563,0.0004869478,0.0001873491],"domain_scores_gemma":[0.9986769,0.000257472,0.000521694,0.0000806918,0.0003881713,0.00007508648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006854746,0.002592193,0.0176784,0.0002532089,0.0006178634,0.00003566016,0.005315448,0.1946038,0.0006996707,0.5317365,0.008461348,0.2373205],"study_design_scores_gemma":[0.0007651194,0.0002129346,0.007277969,0.0006100496,0.00003497676,0.000327038,0.0002412105,0.9783869,0.0001029609,0.0104046,0.001397094,0.0002391068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7343357,0.00006548016,0.2641763,0.0002914011,0.00035616,0.00008460015,8.517208e-7,0.00002553234,0.0006639092],"genre_scores_gemma":[0.7213241,0.00002888336,0.2776273,0.000107103,0.0008477223,0.000001926102,0.000002136697,0.00001102296,0.00004981449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7837831,"threshold_uncertainty_score":0.4683936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02553075317867747,"score_gpt":0.2953977628151206,"score_spread":0.2698670096364432,"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."}}