{"id":"W2038583950","doi":"10.1081/stm-120004469","title":"Generalization of discrete-time geometric bounds to convergence rate of Markov processes on R<sup><i>n</i></sup>","year":2002,"lang":"en","type":"article","venue":"Stochastic Models","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"University of Toronto","keywords":"Mathematics; Markov chain; Spectral gap; Limit (mathematics); Discrete time and continuous time; Path (computing); Operator (biology); Markov process; Generalization; Combinatorics; Convergence (economics); Applied mathematics; Mathematical analysis; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006944314,0.0002651335,0.0005711467,0.000437053,0.00006341701,0.00001825987,0.0002966843,0.0001087588,0.0002413253],"category_scores_gemma":[0.00184806,0.0002347791,0.000106308,0.001195657,0.00007918507,0.0001320618,0.00009094396,0.00009795772,0.000001561488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004478923,"about_ca_system_score_gemma":0.00005080339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002578928,"about_ca_topic_score_gemma":0.000003094829,"domain_scores_codex":[0.9980947,0.0001400576,0.0006440464,0.0003809274,0.0004087931,0.0003314154],"domain_scores_gemma":[0.997815,0.0007494086,0.00030511,0.0005276214,0.0004479917,0.0001548735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004114812,0.0008585147,0.00003557133,0.002921554,0.0002496566,0.000008083616,0.008028386,0.8817405,0.006138892,0.07706016,0.01535895,0.007188227],"study_design_scores_gemma":[0.0006102665,0.0004823241,0.000002901214,0.000315138,0.0001045796,0.0000038857,0.0001117268,0.9746172,0.004726065,0.01858639,0.0000833765,0.0003561287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1118657,0.0001975289,0.8837196,0.00008768745,0.0000780917,0.0005728182,0.00009262325,0.00004450777,0.003341474],"genre_scores_gemma":[0.9582785,0.00004415992,0.03781909,0.0001233849,0.00007231189,0.00007294455,0.00001139882,0.00005355899,0.003524692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8464127,"threshold_uncertainty_score":0.9574014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05987607008197766,"score_gpt":0.3025470344538848,"score_spread":0.2426709643719071,"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."}}