{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006914425,0.0001541313,0.0003707395,0.0001351055,0.00006541178,0.00003563233,0.0001078099,0.00008293347,0.00004186924],"category_scores_gemma":[0.000230191,0.0001185202,0.0001064463,0.0001349099,0.00008399425,0.00007883243,0.000048276,0.0003346457,1.57804e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000290541,"about_ca_system_score_gemma":0.00005737293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006963016,"about_ca_topic_score_gemma":0.000008050381,"domain_scores_codex":[0.9984925,0.0001728176,0.0005262424,0.0001311244,0.0005165594,0.0001607992],"domain_scores_gemma":[0.9978099,0.00117766,0.0003732555,0.00006997736,0.000402397,0.0001667982],"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":[0.001057151,0.0004984923,0.01087675,0.0003381289,0.0006905555,0.0002677001,0.001067308,0.001809975,0.0002580273,0.911483,0.02855194,0.04310098],"study_design_scores_gemma":[0.003244444,0.001509448,0.01639331,0.000210032,0.0001954076,0.00050245,0.0005962853,0.0806874,0.00002713064,0.8927224,0.00343602,0.0004756929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5644001,0.0003655451,0.4332092,0.0005377532,0.0004183269,0.0002195311,0.0002375448,0.00001188042,0.0006001731],"genre_scores_gemma":[0.638027,0.00003944606,0.3614358,0.000201623,0.0001149617,0.000001032009,0.000004319825,0.00001232637,0.000163509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07887742,"threshold_uncertainty_score":0.4833114,"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."}}