{"id":"W4233159442","doi":"10.1109/.2001.980642","title":"Stochastic nonlinear minimax filtering in continuous-time","year":2002,"lang":"en","type":"article","venue":"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Ottawa","funders":"","keywords":"Hamilton–Jacobi–Bellman equation; Minimax; Mathematics; Nonlinear system; Estimator; Applied mathematics; Mathematical optimization; Equivalence (formal languages); Stochastic optimization; Computer science; Bellman equation; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006153335,0.0003882601,0.0006527426,0.0002511571,0.0002051068,0.0004116854,0.001782359,0.0001962605,0.0001640456],"category_scores_gemma":[0.0006218408,0.0002857137,0.0001484282,0.0004422423,0.0001754065,0.0004931998,0.0003380724,0.0003379702,0.0002157228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003896551,"about_ca_system_score_gemma":0.00003545619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001827152,"about_ca_topic_score_gemma":0.000002903358,"domain_scores_codex":[0.9971529,0.00003334765,0.0007589278,0.0008064708,0.0006841388,0.0005642284],"domain_scores_gemma":[0.9978739,0.000489645,0.0004189881,0.0005189281,0.000491157,0.0002073585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002100842,0.00160687,0.004562783,0.0001921845,0.0001553678,0.00003522223,0.002673816,0.001519447,0.4734831,0.0181355,0.02193359,0.4736013],"study_design_scores_gemma":[0.002404832,0.0002845193,0.002568648,0.0005867868,0.00001847752,0.00002552047,0.00004897263,0.9905651,0.0008961953,0.001656243,0.0005687688,0.0003759754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9648728,0.00006465527,0.02806608,0.001633186,0.000995882,0.000706846,0.00004206064,0.0001928294,0.003425708],"genre_scores_gemma":[0.9713895,0.0001327067,0.02652746,0.0007965763,0.000174214,0.00003214679,0.000001071164,0.00002780062,0.0009184804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9890456,"threshold_uncertainty_score":0.9999595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01795243044018799,"score_gpt":0.2201489235966103,"score_spread":0.2021964931564223,"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."}}