{"id":"W3156595930","doi":"10.1007/s11222-022-10080-8","title":"Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics","year":2022,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convergence (economics); Mathematics; Scaling; Algorithm; Limit (mathematics); Applied mathematics; Random walk; Sample (material); Dimension (graph theory); Product (mathematics); Scaling limit; Mathematical optimization; Mathematical analysis; Statistics; Combinatorics","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":[0.01387407,0.001306069,0.001903267,0.001802879,0.001053921,0.003087661,0.002771062,0.001770958,0.003857263],"category_scores_gemma":[0.08484256,0.001105269,0.001012455,0.001352151,0.00337148,0.005434052,0.003153949,0.003392829,0.001055624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001921955,"about_ca_system_score_gemma":0.002307316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001915019,"about_ca_topic_score_gemma":0.001966262,"domain_scores_codex":[0.9941517,0.003530526,0.000276819,0.0007129733,0.001030066,0.0002979408],"domain_scores_gemma":[0.9661736,0.02701247,0.001511507,0.00279428,0.001863578,0.0006446785],"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":[0.0002150422,0.0001688177,0.001802913,0.0003399868,0.0001210123,0.000130015,0.0003158726,0.3968462,0.002702005,0.5367613,0.002734234,0.05786261],"study_design_scores_gemma":[0.00001955826,0.0000304479,0.0001429907,0.00004047317,0.000009936175,0.00002455501,0.00001759623,0.8898942,0.000802831,0.1082355,0.0007622574,0.00001968889],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00925894,0.0004541968,0.9873816,0.000283645,0.00004934005,0.00008429886,0.00003036285,0.0003936875,0.002063931],"genre_scores_gemma":[0.4395972,0.001212149,0.552772,0.0004801066,0.0002485461,0.001034626,0.0002756559,0.0007699252,0.003609653],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01387407,"threshold_uncertainty_score":0.07337397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04183109047495914,"score_gpt":0.3489249357384459,"score_spread":0.3070938452634867,"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."}}