{"id":"W2103340722","doi":"","title":"Ecient Bayesian inference for stochastic volatility models with ensemble MCMC methods","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Markov chain Monte Carlo; Stochastic volatility; Bayesian probability; Univariate; Bayesian inference; Volatility (finance); Econometrics; Computer science; Gibbs sampling; Monte Carlo method; Ensemble forecasting; Mathematics; Algorithm; Statistics; Artificial intelligence; Multivariate statistics","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":[0.006292627,0.001133346,0.001483982,0.001719648,0.0008934984,0.001808099,0.002718709,0.002050144,0.003438679],"category_scores_gemma":[0.03309243,0.0009795757,0.001141863,0.001606061,0.001146652,0.003191957,0.0025362,0.004178447,0.0006898702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001401211,"about_ca_system_score_gemma":0.001962367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01067349,"about_ca_topic_score_gemma":0.01489671,"domain_scores_codex":[0.9969774,0.001654371,0.0001256698,0.000386757,0.0007141542,0.0001416173],"domain_scores_gemma":[0.9850178,0.01190874,0.0005261351,0.001390429,0.0009435572,0.0002132276],"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":[0.000108955,0.00006242606,0.002568211,0.0001037522,0.000189509,0.00007422596,0.00009432754,0.8437087,0.001061419,0.08186411,0.001790498,0.06837396],"study_design_scores_gemma":[0.000008271856,0.000004456072,0.0001015362,0.000008496248,0.000005886403,0.00001205473,0.000003096342,0.9840031,0.0002512255,0.01518666,0.0004089843,0.000006140056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004629939,0.0002936179,0.9940609,0.0001161716,0.00003220528,0.00001874626,0.0000821848,0.000302321,0.0004638078],"genre_scores_gemma":[0.2638808,0.0009119689,0.731262,0.0003404242,0.0002468815,0.0002768012,0.0008733095,0.00044265,0.001765118],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01067349,"threshold_uncertainty_score":0.033279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09728062809597461,"score_gpt":0.2457963658304309,"score_spread":0.1485157377344563,"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."}}