{"id":"W1636009176","doi":"10.1002/nla.800","title":"Fast multilevel methods for Markov chains","year":2011,"lang":"en","type":"article","venue":"Numerical Linear Algebra with Applications","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Israel Science Foundation","keywords":"Markov chain; Speedup; Residual; Mathematics; Algorithm; Iterative method; Markov process; Applied mathematics; Markov model; Computer science; Mathematical optimization; Parallel computing; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009060731,0.0004553391,0.0007535883,0.0007289519,0.0006247233,0.0008108701,0.0009416442,0.0007493626,0.00563101],"category_scores_gemma":[0.003837039,0.0003468518,0.0009088529,0.0007933197,0.0005555101,0.0008120447,0.001459835,0.001509601,0.001384006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006700098,"about_ca_system_score_gemma":0.001210728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004073597,"about_ca_topic_score_gemma":0.004204821,"domain_scores_codex":[0.9993363,0.0002524014,0.00002638791,0.00004849815,0.0002796724,0.00005657104],"domain_scores_gemma":[0.9984363,0.0008403021,0.0001136529,0.0002178602,0.0003131588,0.00007878193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006838573,0.0000416305,0.0007634929,0.0002734177,0.00009681033,0.0001165789,0.0002158042,0.5838642,0.004442969,0.2954907,0.006266069,0.10836],"study_design_scores_gemma":[0.000009919888,0.000007157071,0.00006530813,0.00001351254,0.000003979197,0.00001213212,0.000009062066,0.9645569,0.0004125836,0.03198172,0.002921365,0.000006211379],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004528983,0.0003587512,0.9907041,0.000191856,0.00006427884,0.00004191505,0.0001103607,0.0003156965,0.003684123],"genre_scores_gemma":[0.2371739,0.0009540973,0.7521833,0.0001759505,0.0001576359,0.0004803585,0.0004021294,0.0004398223,0.008032754],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00563101,"threshold_uncertainty_score":0.01883757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02959064902754927,"score_gpt":0.3115063589447691,"score_spread":0.2819157099172198,"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."}}