{"id":"W2165897119","doi":"","title":"Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions","year":2012,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Discriminative model; Identifiability; Markov chain; Conditional probability distribution; Variable-order Markov model; Chain rule (probability); Markov property; Conditional probability; Mathematics; Mixing (physics); Balance equation; Conditional variance; Regular conditional probability; Sequence (biology); Uniqueness; Feature (linguistics); Hidden Markov model; Additive Markov chain; Markov model; Markov process; Computer science; Random variable; Artificial intelligence; Statistics; Econometrics; Probability mass function; Mathematical analysis","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":[0.0002311784,0.0001266475,0.0001451693,0.0001278587,0.00025014,0.0003741908,0.0002376811,0.00006790442,0.000001733193],"category_scores_gemma":[0.00001952509,0.00008999302,0.00002633704,0.0003395109,0.00005950503,0.00552442,0.00003815267,0.000146199,0.00001545399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003738485,"about_ca_system_score_gemma":0.0001186894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001862036,"about_ca_topic_score_gemma":8.677323e-7,"domain_scores_codex":[0.9989634,0.00003832974,0.0003098188,0.0000925283,0.0003589219,0.0002370445],"domain_scores_gemma":[0.9990733,0.00001278717,0.0002934041,0.0001723133,0.0003672197,0.00008096222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004811174,0.0008734522,0.02030831,0.01476774,0.0004092086,0.000006039601,0.1075595,0.112431,0.02812972,0.4239794,0.01905228,0.2720022],"study_design_scores_gemma":[0.0004385621,0.0001049729,0.00133299,0.0005832581,0.00001661586,0.0002455761,0.001349692,0.9909493,0.002156402,0.00006209496,0.002418397,0.0003421669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05090996,0.000512285,0.945635,0.0004418659,0.0004208755,0.000198687,0.000007644649,0.0002164456,0.001657259],"genre_scores_gemma":[0.9971385,0.000001275035,0.002228308,0.000161419,0.00008530685,0.00003850728,0.00002249772,0.000004805282,0.0003194074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9462285,"threshold_uncertainty_score":0.4005071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02706579931201523,"score_gpt":0.225592260297845,"score_spread":0.1985264609858298,"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."}}