{"id":"W4238723945","doi":"10.1017/s0021900200117954","title":"Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms","year":2007,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ergodicity; Mathematics; Counterexample; Markov chain Monte Carlo; Markov chain; Statistical physics; Coupling (piping); Markov chain mixing time; Convergence (economics); Monte Carlo method; Algorithm; Applied mathematics; Markov model; Variable-order Markov model; Discrete mathematics; Statistics; Physics","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":[0.007942821,0.0009539833,0.001364857,0.00157016,0.001176371,0.002081172,0.00213669,0.001374,0.002344527],"category_scores_gemma":[0.04429974,0.0006583271,0.001259883,0.0009497653,0.003452071,0.003533236,0.004465282,0.002716701,0.0003759337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001396213,"about_ca_system_score_gemma":0.001294108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001407666,"about_ca_topic_score_gemma":0.00073671,"domain_scores_codex":[0.9963313,0.001782557,0.0001895046,0.0005157447,0.0008497032,0.0003312484],"domain_scores_gemma":[0.9683355,0.02408538,0.00212982,0.002310489,0.001847552,0.001291339],"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":[0.0001221527,0.00007776385,0.001923359,0.00009992306,0.00009929382,0.000116467,0.0002728541,0.2049289,0.00414103,0.7775363,0.000436791,0.01024507],"study_design_scores_gemma":[0.00002780908,0.00007181094,0.0003698919,0.00002438216,0.00002354084,0.00004568565,0.00002759403,0.7986093,0.001707269,0.1983984,0.0006742488,0.00002008769],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05152847,0.0001691423,0.9437451,0.0002897998,0.00002324213,0.00005539647,0.0000366965,0.000135487,0.004016622],"genre_scores_gemma":[0.9202231,0.0003254503,0.0765617,0.0002018426,0.00008251829,0.0002498257,0.0001182901,0.0001541227,0.002083177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007942821,"threshold_uncertainty_score":0.04200619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05514559893675608,"score_gpt":0.3196763472424737,"score_spread":0.2645307483057177,"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."}}