{"id":"W3138576109","doi":"10.1007/s00180-021-01095-2","title":"Jump Markov chains and rejection-free Metropolis algorithms","year":2021,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Fujitsu","keywords":"Metropolis–Hastings algorithm; Parallel tempering; Markov chain Monte Carlo; Markov chain; Algorithm; Jump; Computer science; Rejection sampling; Bayesian probability; Mathematics; Artificial intelligence; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003679966,0.0001705963,0.0002658995,0.00008397135,0.0001974076,0.00007791586,0.0001145015,0.0000721022,0.0001098167],"category_scores_gemma":[0.00223945,0.0001769006,0.00005015761,0.0002276823,0.00009784832,0.00005346425,0.0001673503,0.000155833,4.627485e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007428508,"about_ca_system_score_gemma":0.00012846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002628487,"about_ca_topic_score_gemma":0.000074976,"domain_scores_codex":[0.998597,0.0001783845,0.000333263,0.0003100667,0.0003631875,0.000218129],"domain_scores_gemma":[0.9970719,0.001912016,0.0001176591,0.0002726945,0.0004870746,0.0001387007],"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":[0.00001421501,0.0001230898,0.0004201629,0.0001609738,0.000115128,0.000122951,0.0003301614,0.0001768816,0.00003582827,0.8610623,0.09360463,0.04383363],"study_design_scores_gemma":[0.001286225,0.00008763529,0.002701249,0.00004416983,0.00009496419,0.0001969496,0.0004854389,0.1442734,0.0001439606,0.8362285,0.01401679,0.0004406947],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003171996,0.0003208671,0.9909928,0.0004222624,0.0004176802,0.0001179176,0.0008330229,0.00006774297,0.003655697],"genre_scores_gemma":[0.006372477,0.00006950983,0.9886968,0.0003032281,0.0002281893,0.00001322944,0.000191101,0.00003412343,0.00409139],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1440966,"threshold_uncertainty_score":0.7213798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05006035503007496,"score_gpt":0.3511342344902775,"score_spread":0.3010738794602025,"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."}}