{"id":"W2999910338","doi":"10.3390/risks8010006","title":"Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Estimator; Monte Carlo method; Markov chain; Conditional probability distribution; Econometrics; Expected shortfall; Hybrid Monte Carlo; Mathematics; Computer science; Statistics; Economics; Finance; Risk management","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001264903,0.0001464311,0.0003368228,0.00002604247,0.0001548097,0.00003979319,0.0001832079,0.00008004539,0.00004847233],"category_scores_gemma":[0.02171201,0.0001268976,0.0000922578,0.0001397039,0.00003511692,0.00003591891,0.00005314282,0.0001914633,0.00001340845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003141909,"about_ca_system_score_gemma":0.00003997513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001096505,"about_ca_topic_score_gemma":0.000003413109,"domain_scores_codex":[0.9985334,0.000451686,0.0004067197,0.0002669684,0.0001065068,0.0002347782],"domain_scores_gemma":[0.993584,0.00567778,0.0002145966,0.0002472843,0.0001245532,0.0001517724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003991297,0.00005449987,0.0006127278,0.0009986119,0.0001288651,0.000002423855,0.002965178,0.0005526736,0.001032982,0.1507605,0.004368683,0.838483],"study_design_scores_gemma":[0.0002753818,0.00008714905,0.0001599289,0.00008964193,0.0001390506,0.000003943102,0.0002693596,0.8770999,0.0002875592,0.1209421,0.000474232,0.0001717592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003346909,0.0001717401,0.9944104,0.000389406,0.00018915,0.000555484,0.0001260316,0.0001316305,0.0006792696],"genre_scores_gemma":[0.07171949,0.00001372951,0.9276651,0.0001376215,0.000191562,0.0001721687,0.000002096524,0.00003040923,0.00006789815],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8765472,"threshold_uncertainty_score":0.9865285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.24751501589596,"score_gpt":0.4897635688588676,"score_spread":0.2422485529629075,"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."}}