{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04630028,0.001153633,0.002181102,0.00313126,0.001719876,0.006664959,0.00366938,0.007221569,0.01005543],"category_scores_gemma":[0.1382574,0.001360643,0.001936309,0.002989105,0.02328488,0.01256299,0.003550682,0.02346035,0.001735444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009204159,"about_ca_system_score_gemma":0.006000668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02315361,"about_ca_topic_score_gemma":0.01077846,"domain_scores_codex":[0.9640675,0.02553032,0.001041014,0.001855438,0.006819488,0.0006862641],"domain_scores_gemma":[0.7125642,0.2691252,0.002079171,0.00611613,0.00872614,0.001389076],"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":[0.00001669247,0.00001663734,0.0001013058,0.00009344248,0.00002478355,0.00003071521,0.0001082452,0.005072227,0.00002411515,0.9763706,0.007950722,0.01019044],"study_design_scores_gemma":[0.00001194743,0.000005099746,0.00006342062,0.0001432672,0.000005007207,0.00001763536,0.00002781303,0.006870077,0.00005478833,0.9751685,0.01761519,0.00001712919],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002503208,0.06912249,0.5832308,0.2853629,0.009240458,0.00006875284,0.0004551843,0.0002119779,0.04980427],"genre_scores_gemma":[0.3057535,0.1186363,0.4463004,0.05552703,0.04784819,0.0005337489,0.0003963971,0.0008672317,0.02413729],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04630028,"threshold_uncertainty_score":0.2448622,"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."}}