{"id":"W1432510334","doi":"10.1016/j.spa.2017.02.002","title":"Elementary bounds on mixing times for decomposable Markov chains","year":2017,"lang":"en","type":"preprint","venue":"Stochastic Processes and their Applications","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain; Mixing (physics); Mathematical proof; Examples of Markov chains; Mathematics; Projection (relational algebra); Simple (philosophy); Markov chain mixing time; Decomposition; Statistical physics; Pure mathematics; Discrete mathematics; Variable-order Markov model; Markov model; Algorithm; Physics; Statistics; Chemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006003063,0.0004681027,0.0005988891,0.0001243702,0.0008498338,0.0002538599,0.0005907261,0.0002286628,0.00001381921],"category_scores_gemma":[0.0004199437,0.000388298,0.0001550345,0.00006700315,0.0001416801,0.00005448257,0.0004761025,0.0003421502,2.369151e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006364253,"about_ca_system_score_gemma":0.0002857063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000223652,"about_ca_topic_score_gemma":0.00007283998,"domain_scores_codex":[0.9982614,0.0000296655,0.0004167547,0.0007467612,0.0001492228,0.0003961766],"domain_scores_gemma":[0.9967478,0.001277144,0.000504258,0.001033398,0.0002862977,0.0001511207],"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":[0.0002758555,0.0009896981,0.000008573305,0.01607618,0.0009529988,0.000001184651,0.002606728,0.00009775747,0.0003819409,0.833041,0.01008305,0.135485],"study_design_scores_gemma":[0.0006306869,0.0001342836,0.00000153121,0.0007875887,0.0002364114,0.000007262904,0.0005612144,0.006964846,0.0004480865,0.9786075,0.01094439,0.0006761722],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006844423,0.001194339,0.9860481,0.0007654619,0.0001689893,0.002953321,0.0007897594,0.0001377394,0.007257871],"genre_scores_gemma":[0.7197624,0.0002568383,0.245516,0.0005092171,0.001960913,0.02357295,0.001000814,0.0003139114,0.007106978],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7405321,"threshold_uncertainty_score":0.9998569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05617056885473928,"score_gpt":0.3640960927168317,"score_spread":0.3079255238620925,"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."}}