{"id":"W1887716531","doi":"10.1109/sfcs.1996.548478","title":"Factoring graphs to bound mixing rates","year":2002,"lang":"en","type":"article","venue":"","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Markov chain; Markov chain Monte Carlo; Mixing (physics); Factorization; Markov chain mixing time; Computer science; Statistical physics; Gibbs sampling; Examples of Markov chains; Markov process; Bounding overwatch; Sampling (signal processing); Algorithm; Monte Carlo method; Mathematics; Variable-order Markov model; Markov model; Statistics; Physics; Artificial intelligence; Machine learning","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":[0.0002827437,0.000114687,0.0001649146,0.00008086614,0.00008093057,0.0000586453,0.0001118558,0.00004358195,0.0004689083],"category_scores_gemma":[0.0003351019,0.00009266643,0.0000752497,0.0001547955,0.00001122048,0.00006080017,0.00005285238,0.00007336274,0.000001924196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002261345,"about_ca_system_score_gemma":0.000002550892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002330581,"about_ca_topic_score_gemma":0.00002745045,"domain_scores_codex":[0.9992499,0.00003946533,0.0001727943,0.0001721938,0.0001296812,0.0002359022],"domain_scores_gemma":[0.9992612,0.0002986475,0.00003000649,0.0002516148,0.00003902619,0.0001195048],"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":[0.0000237296,0.0004109396,0.006750083,0.0003913903,0.000203745,0.00006578478,0.01885425,0.00001001778,0.06086552,0.6455674,0.1612952,0.1055619],"study_design_scores_gemma":[0.002070904,0.0005062906,0.0006662686,0.0003718486,0.0001513625,0.00005413346,0.004861666,0.006059273,0.2106833,0.1347016,0.6372571,0.002616312],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7233223,0.0001536396,0.04366784,0.0006653562,0.0004842811,0.0002821457,0.000002426073,0.0002462316,0.2311758],"genre_scores_gemma":[0.7270171,0.00002696993,0.2280995,0.0006420769,0.0001492851,0.000025969,5.409966e-7,0.00004292131,0.0439956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5108658,"threshold_uncertainty_score":0.5134217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1669340737502711,"score_gpt":0.3785547397536901,"score_spread":0.211620666003419,"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."}}