{"id":"W1968782267","doi":"10.1002/cjs.5550360401","title":"Optimal scaling of Metropolis algorithms: Heading toward general target distributions","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Heading (navigation); Simple (philosophy); Scaling; Independent and identically distributed random variables; Gaussian; Algorithm; Distribution (mathematics); Asymptotically optimal algorithm; Computer science; Mathematics; Mathematical optimization; Statistical physics; Applied mathematics; Random variable; Statistics; Mathematical analysis; Geometry; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0006038198,0.0001487811,0.0004590616,0.000271686,0.000192498,0.0000222254,0.0002127039,0.00007878867,0.00009901622],"category_scores_gemma":[0.001676544,0.000141674,0.0001342456,0.0002083342,0.0002196784,0.00008128808,0.00001465643,0.0002696603,9.996607e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002862773,"about_ca_system_score_gemma":0.001186232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002112891,"about_ca_topic_score_gemma":0.0008939753,"domain_scores_codex":[0.9984569,0.0001119887,0.0007082264,0.0001015557,0.0002495016,0.0003718499],"domain_scores_gemma":[0.9977834,0.0003274938,0.0004195821,0.0001554375,0.0006244195,0.0006897292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009144593,0.0002468949,0.01254412,0.0006869725,0.001067754,0.005506177,0.01287535,0.002974241,0.001363246,0.6157585,0.3285169,0.01836832],"study_design_scores_gemma":[0.01709078,0.005611602,0.01319519,0.003036597,0.004004675,0.02279791,0.02481069,0.1971476,0.06628799,0.2776954,0.3607969,0.007524664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08505029,0.0003932927,0.9112486,0.0001256659,0.0004733017,0.00006776381,0.002193364,0.000004745452,0.0004429825],"genre_scores_gemma":[0.2180873,0.00005737363,0.7813322,0.00002980999,0.0002619315,9.075584e-7,0.00002028874,0.00002151926,0.0001886929],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3380632,"threshold_uncertainty_score":0.5777298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09391328041418588,"score_gpt":0.3312765790653092,"score_spread":0.2373632986511233,"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."}}