{"id":"W2977245197","doi":"10.1111/rssb.12464","title":"Non-Reversible Parallel Tempering: A Scalable Highly Parallel MCMC Scheme","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Parallel tempering; Markov chain Monte Carlo; Computer science; Markov chain; Scalability; Prior probability; Scaling; Piecewise; Algorithm; Range (aeronautics); Applied mathematics; Schedule; Simulated annealing; Distribution (mathematics); Mathematical optimization; Statistical physics; Mathematics; Bayesian probability; Hybrid Monte Carlo; Artificial intelligence; Physics; Materials science; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004881768,0.000535173,0.001612367,0.00004357626,0.0005200792,0.0001487882,0.0007917576,0.0004724543,0.000826855],"category_scores_gemma":[0.02060581,0.0003730808,0.0008145639,0.0004350192,0.0009809408,0.0001736844,0.0006133054,0.001561045,0.000002696098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000262408,"about_ca_system_score_gemma":0.0005588543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004779077,"about_ca_topic_score_gemma":0.00002475595,"domain_scores_codex":[0.9936337,0.002238241,0.001622101,0.000562757,0.0009697048,0.0009734456],"domain_scores_gemma":[0.9856582,0.01145046,0.0007642623,0.0006939822,0.0008189589,0.0006141245],"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":[0.001012051,0.0006660595,0.001089074,0.0008924954,0.001254557,0.0006471642,0.001043407,0.0004633317,0.003128672,0.7391499,0.2464904,0.004162912],"study_design_scores_gemma":[0.00681659,0.00171338,0.005371195,0.0005801109,0.002032059,0.001459339,0.004762959,0.02218142,0.004326745,0.8237901,0.1250003,0.001965758],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006662536,0.0003407865,0.9855716,0.003539912,0.001443291,0.0002927312,0.0002370216,0.00004404309,0.001868133],"genre_scores_gemma":[0.004239119,0.0001377245,0.9878033,0.0009849616,0.0004846051,0.00001966799,0.00001114635,0.000077663,0.006241818],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1214901,"threshold_uncertainty_score":0.9998721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09901716239151234,"score_gpt":0.3612658349096236,"score_spread":0.2622486725181112,"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."}}