{"id":"W4386144974","doi":"10.1007/978-3-030-19071-2_104-1","title":"How Markov’s Little Idea Transformed Statistics","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Probability and Statistical Research","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Markov chain Monte Carlo; Markov chain; Variable-order Markov model; Computer science; Markov model; Bayesian probability; Markov chain mixing time; Mathematics; Statistical physics; Statistics; Artificial intelligence; Physics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006701383,0.0003857211,0.0006420698,0.0001441416,0.0001044783,0.0001761573,0.0003696501,0.0004863464,0.003757316],"category_scores_gemma":[0.003090592,0.0003238731,0.0001748592,0.0000466448,0.0002868237,0.00006471791,0.000110781,0.0008050704,0.0009154388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001357042,"about_ca_system_score_gemma":0.0002515093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001484809,"about_ca_topic_score_gemma":0.0005684798,"domain_scores_codex":[0.9974561,0.00003857145,0.000468343,0.0004853006,0.001009325,0.0005424028],"domain_scores_gemma":[0.9940701,0.004708643,0.00008510356,0.0005896372,0.0002609903,0.0002855281],"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":[0.0000282208,0.00002151657,2.943493e-7,0.000890128,0.00008835359,0.00008112161,0.00004342188,2.657386e-8,0.000001994862,0.8647094,0.1021323,0.03200327],"study_design_scores_gemma":[0.0001969676,0.00009936252,0.00000508147,0.0001094246,0.00006727263,0.00000336893,0.00001593275,0.0001584096,0.00001147896,0.8826607,0.11635,0.0003220432],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[6.86071e-7,0.00002980584,0.2492884,0.001369169,0.0001391006,0.0005979912,0.002807645,0.0002435049,0.7455237],"genre_scores_gemma":[0.00004627015,0.0002999392,0.06677061,0.00005513204,0.0001047297,0.00003261523,0.0002098597,0.0001335765,0.9323473],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1868235,"threshold_uncertainty_score":0.9999213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.150870108139301,"score_gpt":0.3592722762189131,"score_spread":0.2084021680796121,"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."}}