{"id":"W2949914592","doi":"10.48550/arxiv.1702.03098","title":"Estimation of Risk Contributions with MCMC","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Eidgenössische Technische Hochschule Zürich; Japan Society for the Promotion of Science; Keio University","keywords":"Estimator; Markov chain Monte Carlo; Value at risk; Econometrics; Monte Carlo method; Mean squared error; Consistency (knowledge bases); Statistics; Asymptotic distribution; Mathematics; Computer science; Risk management; Economics; Finance; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004395822,0.001144182,0.001479789,0.002357172,0.0009125237,0.001455445,0.002270393,0.001725781,0.004824444],"category_scores_gemma":[0.0261926,0.001232274,0.001189252,0.001861373,0.001267612,0.001736642,0.001674065,0.002733825,0.0007928183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001608427,"about_ca_system_score_gemma":0.002960241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01538983,"about_ca_topic_score_gemma":0.01720356,"domain_scores_codex":[0.9984932,0.0006530563,0.00009507972,0.0002467342,0.0003892816,0.000122656],"domain_scores_gemma":[0.985718,0.01091404,0.0008878854,0.001141056,0.001060285,0.000278718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001059604,0.00005696449,0.003740049,0.00009326486,0.0001073649,0.0001059076,0.00007414183,0.9191146,0.0007964501,0.03434798,0.001330501,0.04012684],"study_design_scores_gemma":[0.000007250798,0.000004514958,0.0001938808,0.000009802875,0.00000576983,0.0000109836,0.000004039192,0.9901821,0.0002297923,0.009081109,0.0002639173,0.000006898342],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01796108,0.0002626554,0.9795344,0.000184146,0.00004524698,0.00008482313,0.0001829294,0.0005780423,0.001166601],"genre_scores_gemma":[0.4312688,0.0004860213,0.5622914,0.00029828,0.0001590287,0.0004858579,0.001280417,0.0003183277,0.003411728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01538983,"threshold_uncertainty_score":0.03060049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1201166665904634,"score_gpt":0.2678759514799738,"score_spread":0.1477592848895104,"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."}}