{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006121722,0.0002409211,0.0004935587,0.0001406105,0.0002014682,0.00003102203,0.0004843279,0.0002862254,0.0000221915],"category_scores_gemma":[0.001013855,0.0002306363,0.000198293,0.00009924051,0.0001965951,0.00009518768,0.0003906731,0.0004707599,4.526102e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001249482,"about_ca_system_score_gemma":0.0001686177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002538743,"about_ca_topic_score_gemma":0.0001881523,"domain_scores_codex":[0.9988423,0.0002190323,0.0002055312,0.0004503077,0.00008153548,0.0002012987],"domain_scores_gemma":[0.9969366,0.0004915025,0.000892401,0.001255635,0.0003201318,0.0001037039],"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":[0.0003853923,0.0003745322,0.004654561,0.0008308282,0.0007266391,0.000188834,0.0008221567,0.1288417,0.00003617031,0.8575327,0.001127737,0.004478812],"study_design_scores_gemma":[0.002564263,0.0001676311,0.001087098,0.000970513,0.002076594,0.000008887191,0.0003605331,0.4530159,0.001440322,0.5366557,0.0007214633,0.0009310997],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4385533,0.00002384364,0.5544298,0.00001851738,0.0001451679,0.0002952437,0.0003045471,0.00006326679,0.006166345],"genre_scores_gemma":[0.9827428,0.0001420015,0.01571685,0.000003943226,0.00004509574,0.000001343678,0.00003755045,0.00002303053,0.001287384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5441895,"threshold_uncertainty_score":0.9405074,"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."}}