{"id":"W2072760811","doi":"10.1080/15598608.2011.10412028","title":"A Characterization of Categorical Markov Chains","year":2011,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Mathematics; Markov chain; Categorical variable; Covariate; Monomial; Characterization (materials science); Applied mathematics; Extension (predicate logic); Representation (politics); Additive Markov chain; Discrete mathematics; Combinatorics; Variable-order Markov model; Statistics; Markov model; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007288377,0.0007070185,0.001668315,0.004785503,0.002244057,0.005691488,0.003250425,0.002891593,0.01044156],"category_scores_gemma":[0.05908606,0.001224906,0.00188108,0.005809006,0.004508498,0.01283191,0.003491403,0.005463551,0.001048595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002766051,"about_ca_system_score_gemma":0.002157171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002772514,"about_ca_topic_score_gemma":0.001977922,"domain_scores_codex":[0.9942943,0.002490296,0.0004705321,0.001231712,0.0009896832,0.0005233898],"domain_scores_gemma":[0.9056582,0.07405711,0.006334136,0.006607261,0.004814953,0.002528285],"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":[0.0000183284,0.00001611304,0.0009235027,0.00002750598,0.000009635584,0.00003621526,0.0002746723,0.002773486,0.0001573626,0.9912658,0.0005425579,0.003954906],"study_design_scores_gemma":[0.00000839381,0.000008780362,0.0002691146,0.0000235736,0.000008554272,0.00006458732,0.00005810191,0.0398688,0.0000606001,0.9583992,0.001218446,0.00001170607],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05844095,0.0007395738,0.9245856,0.002898111,0.0000667308,0.0001194336,0.001335482,0.0003614037,0.0114526],"genre_scores_gemma":[0.7578714,0.001514165,0.2276916,0.00111612,0.00059593,0.0008232737,0.003034732,0.0003301215,0.007022592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01044156,"threshold_uncertainty_score":0.03854513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04208736102694741,"score_gpt":0.2970789345771412,"score_spread":0.2549915735501938,"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."}}