{"id":"W3121945553","doi":"","title":"Estimation of Risk Contributions with MCMC","year":2017,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov chain Monte Carlo; Estimator; Econometrics; Monte Carlo method; Computer science; Value at risk; Consistency (knowledge bases); Statistics; Mathematics; Risk management; Finance; Economics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004559897,0.001196893,0.001575694,0.0024345,0.0009332689,0.001486576,0.00257248,0.001949082,0.005285481],"category_scores_gemma":[0.02650586,0.001297179,0.001334805,0.002059531,0.001223456,0.001765378,0.001738011,0.002885029,0.0009060912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00154814,"about_ca_system_score_gemma":0.003035289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01830723,"about_ca_topic_score_gemma":0.01964208,"domain_scores_codex":[0.998487,0.0006369061,0.0001035386,0.0002465262,0.0003926638,0.0001332942],"domain_scores_gemma":[0.9850482,0.01128342,0.0009014649,0.001291441,0.001185611,0.0002898278],"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":[0.0001198817,0.00006265558,0.004372233,0.00009704205,0.000130923,0.0001220621,0.00007847395,0.9220781,0.0007956185,0.02780594,0.001496537,0.04284055],"study_design_scores_gemma":[0.000008677155,0.000004923428,0.000220132,0.000009680416,0.000006744936,0.00001139506,0.000004438303,0.99188,0.0002152385,0.007363858,0.0002678436,0.000007161756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02256217,0.0003258787,0.9743887,0.0002178662,0.00005574797,0.000109898,0.0002564057,0.0007863206,0.001297024],"genre_scores_gemma":[0.4320193,0.0004855038,0.5608858,0.0003392925,0.0001740406,0.0005859833,0.001649196,0.0003620244,0.003498788],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01830723,"threshold_uncertainty_score":0.03640133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02861244360764767,"score_gpt":0.3684579645489457,"score_spread":0.3398455209412981,"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."}}