{"id":"W2795296448","doi":"10.1016/j.spa.2018.03.016","title":"Linear Volterra backward stochastic integral equations","year":2018,"lang":"en","type":"article","venue":"Stochastic Processes and their Applications","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Volterra integral equation; Kernel (algebra); Brownian motion; Integral equation; Mathematical analysis; Measure (data warehouse); Poisson distribution; Stochastic integral; Malliavin calculus; Stochastic process; Geometric Brownian motion; Applied mathematics; Stochastic differential equation; Diffusion process; Pure mathematics; Partial differential equation; Stochastic partial differential equation; Statistics","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":[0.001508832,0.001223458,0.001934986,0.001202934,0.0005555432,0.002607645,0.001427056,0.0028963,0.006255936],"category_scores_gemma":[0.005344954,0.0007574178,0.001157427,0.001869945,0.001762062,0.002576229,0.001701958,0.002856617,0.001121896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001581264,"about_ca_system_score_gemma":0.001959301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006061683,"about_ca_topic_score_gemma":0.003372115,"domain_scores_codex":[0.9993889,0.0002129,0.00003348606,0.00008758955,0.0002224809,0.00005470384],"domain_scores_gemma":[0.9981741,0.001136224,0.0001776001,0.00007140597,0.0003607041,0.00007999423],"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":[0.00001925317,0.00004622227,0.0003297931,0.0001006655,0.00003391925,0.00009518498,0.0001375514,0.1077533,0.001000834,0.8745369,0.002359263,0.01358716],"study_design_scores_gemma":[0.00001754496,0.000008535926,0.0002021942,0.00002981144,0.00002504442,0.00009346897,0.000029563,0.6215141,0.0003207478,0.374197,0.003536249,0.00002569159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0185931,0.004444051,0.951254,0.002392198,0.0004996969,0.00003707266,0.0003469778,0.0002407702,0.02219213],"genre_scores_gemma":[0.6788669,0.01046676,0.1406153,0.001454609,0.001285967,0.0003153826,0.0009783736,0.0004439264,0.1655728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006255936,"threshold_uncertainty_score":0.02092814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02967928173220958,"score_gpt":0.2444737530179488,"score_spread":0.2147944712857392,"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."}}