{"id":"W4283793874","doi":"10.1002/cjs.11707","title":"Reflections on Bayesian inference and Markov chain Monte Carlo","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Bayesian inference; Inference; Bayesian probability; Monte Carlo method; Computer science; Markov chain; Hybrid Monte Carlo; Variable-order Bayesian network; Statistical physics; Econometrics; Artificial intelligence; Mathematics; Statistics; Machine learning; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0008178339,0.0001236342,0.0002448577,0.0003209547,0.0004069787,0.00004930501,0.0001632447,0.00003610019,0.0001734145],"category_scores_gemma":[0.001268541,0.0001239945,0.0000447345,0.0001682223,0.00007045418,0.00003919096,0.0000245292,0.0004781524,4.353706e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002751508,"about_ca_system_score_gemma":0.0007549694,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001612338,"about_ca_topic_score_gemma":0.03104408,"domain_scores_codex":[0.9988094,0.0002372289,0.0003613191,0.0001140357,0.0002216452,0.0002563779],"domain_scores_gemma":[0.9982467,0.0006342985,0.0002437599,0.0001677364,0.0001667491,0.0005408024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001272233,0.0001009979,0.002525073,0.0001782938,0.0002359787,0.002542891,0.009972395,0.0007212454,0.0001518558,0.7280429,0.1758261,0.0795751],"study_design_scores_gemma":[0.004255723,0.00597934,0.003740564,0.0004376112,0.0006500093,0.003388064,0.02021287,0.01631914,0.0001368177,0.3164434,0.6264491,0.001987379],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1318761,0.001289882,0.8082653,0.004388848,0.004729345,0.000831571,0.005112321,0.00004619565,0.0434604],"genre_scores_gemma":[0.8377349,0.00004536969,0.1596888,0.0004930524,0.0001416655,0.000008339198,0.000002885498,0.00003319171,0.00185178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7058588,"threshold_uncertainty_score":0.9866368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0827219781909236,"score_gpt":0.3648240099370348,"score_spread":0.2821020317461113,"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."}}