{"id":"W3104148044","doi":"","title":"On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Midpoint; Discretization; Ergodicity; Mathematics; Midpoint method; Asymptotic distribution; Langevin equation; Applied mathematics; Stationary distribution; Mathematical analysis; Mathematical optimization; Statistical physics; Geometry; Statistics; Physics; Estimator","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":[0.01710256,0.0007987931,0.001461378,0.001751387,0.0009790754,0.00172793,0.00224299,0.001561685,0.002402387],"category_scores_gemma":[0.09853845,0.000553223,0.001177235,0.0009430894,0.005081881,0.00311802,0.003083307,0.003199046,0.000492261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001524551,"about_ca_system_score_gemma":0.002116503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002864248,"about_ca_topic_score_gemma":0.001270613,"domain_scores_codex":[0.9949008,0.002761012,0.0001998918,0.0005713651,0.001275923,0.0002909716],"domain_scores_gemma":[0.9313774,0.05430211,0.003959712,0.004498802,0.005009461,0.0008524088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001983059,0.00006694526,0.004507471,0.0001878782,0.00007442418,0.0002426354,0.0002914469,0.2446918,0.00418918,0.7301143,0.0009533447,0.01448222],"study_design_scores_gemma":[0.00001884055,0.00003106516,0.0003589998,0.00004476609,0.00001213487,0.00004799481,0.00002297854,0.900109,0.001233558,0.09771153,0.0003852553,0.00002393847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02494176,0.0004752309,0.9714012,0.0004272129,0.00005220075,0.00004669994,0.00006449191,0.0002353039,0.002355969],"genre_scores_gemma":[0.7809576,0.00114436,0.213498,0.0004198176,0.0002517934,0.0004503334,0.0003102206,0.0003703505,0.002597502],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01710256,"threshold_uncertainty_score":0.09044802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3249807132132427,"score_gpt":0.2792546313757626,"score_spread":0.04572608183748006,"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."}}