{"id":"W2962861789","doi":"10.1080/10618600.2019.1598872","title":"Adaptive Incremental Mixture Markov Chain Monte Carlo","year":2019,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Center for Advanced Study, University of Illinois at Urbana-Champaign; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Center for Advanced Study in the Behavioral Sciences, Stanford University; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Insight SFI Research Centre for Data Analytics; Science Foundation Ireland; National Institutes of Health; National Science Foundation","keywords":"Markov chain Monte Carlo; Computer science; Kernel (algebra); Mixture model; Gaussian process; Mathematics; Bayesian probability; Semiparametric regression; Algorithm; Mathematical optimization; Parametric statistics; Artificial intelligence; Gaussian; Statistics","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":[0.004222916,0.0009847685,0.001862422,0.001334797,0.0008046984,0.001481338,0.004412352,0.001666873,0.003385107],"category_scores_gemma":[0.01702518,0.001100292,0.001506994,0.00147233,0.001469885,0.002320395,0.002362618,0.00297675,0.0009828391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001318572,"about_ca_system_score_gemma":0.00211432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006102916,"about_ca_topic_score_gemma":0.009020966,"domain_scores_codex":[0.9980335,0.0009712811,0.00007263166,0.0003261045,0.0004430003,0.0001536671],"domain_scores_gemma":[0.990137,0.007382311,0.0005664429,0.0008572406,0.0007791409,0.0002778666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001567057,0.00008235068,0.002205466,0.0001203918,0.0001153333,0.0001563364,0.0001310951,0.8508561,0.0009008913,0.08941662,0.002511444,0.05334724],"study_design_scores_gemma":[0.00001157564,0.000007774229,0.00005773651,0.000006865288,0.000007094979,0.00001441201,0.00000305436,0.9829146,0.0001759787,0.01625601,0.0005367874,0.000008127455],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003894842,0.0001550733,0.9945773,0.000118918,0.00003189518,0.00005230577,0.00006382021,0.0004285113,0.0006774169],"genre_scores_gemma":[0.2906869,0.0003927007,0.703977,0.0003436937,0.0001530272,0.0006099304,0.0008339838,0.0003589911,0.00264363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006102916,"threshold_uncertainty_score":0.02233315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02149832176227343,"score_gpt":0.298824804278164,"score_spread":0.2773264825158905,"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."}}