{"id":"W2297082043","doi":"10.1080/10618600.2018.1513365","title":"Adaptive Component-Wise Multiple-Try Metropolis Sampling","year":2018,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","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":"Metropolis–Hastings algorithm; Markov chain; Ergodicity; Markov chain Monte Carlo; Component (thermodynamics); Algorithm; Computer science; Mathematics; Set (abstract data type); Additive Markov chain; Mathematical optimization; Distribution (mathematics); Variable-order Markov model; Markov model; Artificial intelligence; Machine learning; Statistics; Bayesian probability","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":[0.00263375,0.0009748701,0.001989628,0.0008316701,0.000710651,0.001597575,0.00353577,0.001681016,0.006253346],"category_scores_gemma":[0.01377231,0.0007556012,0.001087517,0.001273173,0.001648519,0.001917255,0.001759389,0.002631454,0.001776856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008499977,"about_ca_system_score_gemma":0.001526084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002987014,"about_ca_topic_score_gemma":0.004149374,"domain_scores_codex":[0.9980493,0.0009762318,0.00007035665,0.0004114415,0.0003475649,0.0001452043],"domain_scores_gemma":[0.9936573,0.00432587,0.0002720156,0.0009951807,0.000579306,0.0001702784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005632002,0.00021636,0.003378226,0.0003349004,0.0002074302,0.0003884316,0.0003104448,0.6241641,0.006387649,0.2369486,0.007246425,0.1198543],"study_design_scores_gemma":[0.00003439984,0.00002545726,0.0001202975,0.00001047949,0.00001395078,0.00005386387,0.000009941595,0.9693003,0.00149207,0.02761735,0.001302524,0.00001929772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01019331,0.0002166968,0.9871114,0.0001176668,0.00005621596,0.0001466291,0.0000748692,0.0006779204,0.001405221],"genre_scores_gemma":[0.3892712,0.0003408031,0.6011272,0.0002878491,0.0001349829,0.001000527,0.0006121523,0.0004751922,0.006750141],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006253346,"threshold_uncertainty_score":0.0209195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08135597292753999,"score_gpt":0.3687627186434692,"score_spread":0.2874067457159292,"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."}}