{"id":"W4412330946","doi":"10.1017/s174849951700025","title":"Conditional Monte Carlo for sums, with applications to insurance and finance","year":2018,"lang":"en","type":"article","venue":"","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Monte Carlo method; Econometrics; Actuarial science; Economics; Computer science; Statistical physics; Mathematics; Statistics; Physics","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":[0.008968567,0.002107103,0.002303686,0.002773482,0.00133366,0.002544024,0.002649655,0.002786496,0.01130563],"category_scores_gemma":[0.03877902,0.001120473,0.001968723,0.003757678,0.004499808,0.003879834,0.003387656,0.005149808,0.001702418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002147056,"about_ca_system_score_gemma":0.002248247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009264884,"about_ca_topic_score_gemma":0.009019078,"domain_scores_codex":[0.9967381,0.002158617,0.0001312062,0.0003350367,0.0004702668,0.0001667907],"domain_scores_gemma":[0.9701783,0.02652377,0.0008802915,0.0009377833,0.001036704,0.0004431467],"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":[0.00005798636,0.00005913523,0.0009644603,0.0002482062,0.0001246743,0.0001420264,0.0001103359,0.194233,0.0001937956,0.7688395,0.003940443,0.03108646],"study_design_scores_gemma":[0.0000196419,0.00002132076,0.0001299645,0.00007604592,0.00002446858,0.00006203769,0.00001906149,0.4705663,0.0001308626,0.524013,0.004910268,0.00002711676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001520607,0.003216853,0.9899086,0.0009117759,0.0003246912,0.000059161,0.0001194821,0.0002676063,0.003671166],"genre_scores_gemma":[0.1615457,0.01228845,0.792892,0.001543096,0.002058814,0.001372584,0.001237284,0.0009551733,0.02610691],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01130563,"threshold_uncertainty_score":0.04743087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09854785864549534,"score_gpt":0.3863882448993676,"score_spread":0.2878403862538722,"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."}}