{"id":"W2149171877","doi":"10.1109/acc.2008.4586678","title":"Stochastic consensus seeking with measurement noise: Convergence and asymptotic normality","year":2008,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Stochastic approximation; Normalization (sociology); Rate of convergence; Mathematics; Convergence (economics); Limit (mathematics); Normality; Spanning tree; Applied mathematics; Noise (video); Convergence of random variables; Computer science; Mathematical optimization; Discrete mathematics; Random variable; Mathematical analysis; Statistics; Artificial intelligence","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":[0.0007628961,0.0002142738,0.000289852,0.00007230996,0.0002333877,0.0001165576,0.0005289229,0.00004770627,0.0000135396],"category_scores_gemma":[0.0001606826,0.0001657174,0.00003884862,0.0003084407,0.0001423139,0.0002238986,0.0001564407,0.0001114722,0.00006285991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000108861,"about_ca_system_score_gemma":0.0001818294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001529007,"about_ca_topic_score_gemma":0.0000620178,"domain_scores_codex":[0.997503,0.0001392064,0.0003734138,0.0005539746,0.001026679,0.0004037096],"domain_scores_gemma":[0.9983755,0.0001369105,0.0001600576,0.0006726637,0.0004298329,0.0002250797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001076656,0.002325518,0.6002564,0.0008257702,0.002633506,0.003519732,0.01262324,0.0662147,0.05815693,0.2145925,0.01750186,0.02027323],"study_design_scores_gemma":[0.003668818,0.0003024179,0.2462706,0.000123066,0.00004655558,0.001370985,0.0001428758,0.745667,0.0007927158,0.0002373812,0.0005005477,0.0008769997],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08780834,0.0001521183,0.9098317,0.000551158,0.0002568655,0.0003677649,0.000004205262,0.0001979472,0.0008298722],"genre_scores_gemma":[0.9959453,0.000001989579,0.00366713,0.0001678747,0.0000311586,0.00002159367,9.619196e-7,0.000008271643,0.000155715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.908137,"threshold_uncertainty_score":0.6757761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03183579041146832,"score_gpt":0.2083232954095606,"score_spread":0.1764875049980923,"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."}}