{"id":"W1737070927","doi":"10.1080/01621459.2015.1096787","title":"Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations","year":2015,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Office of Science; Advanced Scientific Computing Research; U.S. Department of Energy","keywords":"Markov chain Monte Carlo; Ergodicity; Importance sampling; Inference; Monte Carlo method; Convergence (economics); Gaussian process; Ode; Rejection sampling","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":[0.005983412,0.001209664,0.001745002,0.001376195,0.0009167892,0.001328874,0.003093245,0.001328087,0.004374203],"category_scores_gemma":[0.02738023,0.0009472236,0.001238629,0.001410088,0.001975241,0.002005706,0.002743863,0.003832951,0.001195633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002130493,"about_ca_system_score_gemma":0.004021254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009847834,"about_ca_topic_score_gemma":0.01613588,"domain_scores_codex":[0.9980395,0.0008909802,0.00008777395,0.0002818604,0.0005621357,0.0001376429],"domain_scores_gemma":[0.9879622,0.008689043,0.000664069,0.001627495,0.0007541677,0.0003031007],"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":[0.00009851236,0.00007641284,0.001673721,0.000148991,0.00008235216,0.0001126011,0.0001634311,0.831586,0.002256931,0.1167255,0.001405104,0.04567062],"study_design_scores_gemma":[0.000008681932,0.000009047733,0.00005894403,0.00000812627,0.000005973278,0.00001166881,0.000005509275,0.9807003,0.000486436,0.01825795,0.0004419342,0.00000548599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002491777,0.00007248847,0.9965475,0.00007415732,0.00001174102,0.00002452313,0.00002374609,0.0003643713,0.0003896295],"genre_scores_gemma":[0.167808,0.0003132933,0.8287966,0.0002064562,0.00008293198,0.0004021935,0.0002641975,0.0004516448,0.001674687],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009847834,"threshold_uncertainty_score":0.03164369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1125788164123556,"score_gpt":0.3749705992627148,"score_spread":0.2623917828503592,"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."}}