{"id":"W2952051563","doi":"10.1007/s11009-015-9449-4","title":"A Convolution Method for Numerical Solution of Backward Stochastic Differential Equations","year":2015,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Stochastic differential equation; Discretization; Convolution (computer science); Fourier transform; Fast Fourier transform; Numerical analysis; Applied mathematics; Extension (predicate logic); Euler method; Euler's formula; Mathematical analysis; Algorithm; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002762324,0.0001166379,0.0004982402,0.000105262,0.00008585311,0.000008418128,0.0001195656,0.0001525526,0.000004880275],"category_scores_gemma":[0.001449241,0.0001334867,0.0000545998,0.0002488987,0.0001305279,0.00002587729,0.00008349435,0.0001375031,0.00000477847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006993319,"about_ca_system_score_gemma":0.00004770101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001817904,"about_ca_topic_score_gemma":0.00001323298,"domain_scores_codex":[0.9986014,0.00005767668,0.0006525411,0.0004338605,0.00002938623,0.0002251528],"domain_scores_gemma":[0.9980401,0.001328602,0.0003232955,0.00017941,0.00006716068,0.00006137788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001265815,0.0001250741,0.0006723361,0.00005863781,0.0000122717,2.360527e-8,0.0005665703,0.003107566,0.0001032451,0.9874873,0.00000650443,0.007733948],"study_design_scores_gemma":[0.0006493382,0.00006801986,0.005699627,0.000005041992,0.00000958786,0.00000121021,0.0000378894,0.2691965,0.00003630837,0.7241509,0.00004377644,0.0001017968],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01094599,0.0002420875,0.9874861,0.0001770512,0.0001664442,0.0006739525,0.00003535812,0.00002671357,0.0002462643],"genre_scores_gemma":[0.5754366,4.797017e-7,0.4243828,0.00002138762,0.00003739804,0.0001029068,0.00001067601,0.00000520427,0.000002484732],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5644906,"threshold_uncertainty_score":0.5443431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1875269846490666,"score_gpt":0.3307519051053052,"score_spread":0.1432249204562386,"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."}}