{"id":"W3092800917","doi":"10.1109/tsp.2021.3104981","title":"Multi-Agent Estimation and Filtering for Minimizing Team Mean-Squared Error","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimum mean square error; Mean squared error; Kalman filter; Mathematics; Computer science; Estimation; Mathematical optimization; Algorithm; Statistics; Control theory (sociology); Estimator; Control (management); Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002022272,0.001071355,0.001463642,0.0006271388,0.0004892944,0.001109829,0.001078864,0.001800439,0.001392272],"category_scores_gemma":[0.008376333,0.0005452132,0.0007959525,0.0008657182,0.001054583,0.00177195,0.001210532,0.001305835,0.000339213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001264094,"about_ca_system_score_gemma":0.001563719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006401294,"about_ca_topic_score_gemma":0.004328656,"domain_scores_codex":[0.9987463,0.0004157269,0.0000747014,0.0003342767,0.0003225662,0.0001065179],"domain_scores_gemma":[0.9969162,0.002129285,0.0003399759,0.0001631714,0.000383577,0.00006771265],"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":[0.00003403503,0.00001448646,0.0003135754,0.00004558371,0.00002999573,0.00002734442,0.00005396941,0.9746205,0.0009670745,0.01085885,0.0004486593,0.01258592],"study_design_scores_gemma":[0.00000368255,0.000009943245,0.00004898449,0.00000339501,0.000003054875,0.000004130598,0.000004120258,0.9963396,0.0002620739,0.003200282,0.000117353,0.000003494671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003506468,0.00006383183,0.9958509,0.00008008283,0.00001423636,0.000009979418,0.00001246315,0.00004715888,0.0004147734],"genre_scores_gemma":[0.6001163,0.0003879337,0.3947537,0.0001637901,0.0001242675,0.0002690944,0.0001981276,0.0001105209,0.003876348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006401294,"threshold_uncertainty_score":0.0127281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04673251531580367,"score_gpt":0.2898797179836596,"score_spread":0.243147202667856,"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."}}