{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002383544,0.0007607812,0.001185525,0.0004065119,0.0003368555,0.001470597,0.0007516709,0.001467275,0.001066665],"category_scores_gemma":[0.00475953,0.0004070485,0.0005290497,0.0006363231,0.0019599,0.001563029,0.001141217,0.001221139,0.0001560302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001489046,"about_ca_system_score_gemma":0.0008438255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002570697,"about_ca_topic_score_gemma":0.001529803,"domain_scores_codex":[0.9989794,0.0004353855,0.00005233875,0.0001983881,0.0002532215,0.00008131408],"domain_scores_gemma":[0.9980703,0.001442708,0.000215323,0.00005864968,0.0001536266,0.00005939765],"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":[0.00006078902,0.00002713933,0.0003861468,0.0001190199,0.00005525279,0.0001284202,0.0000962161,0.7178105,0.001236084,0.2685611,0.0005746008,0.01094471],"study_design_scores_gemma":[0.000009610498,0.00002259171,0.0001084202,0.000009136862,0.000005957684,0.00001041373,0.00000893691,0.9418367,0.0001859652,0.05736054,0.0004338447,0.000007975167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02214327,0.001039908,0.9713987,0.0006227495,0.00008050519,0.00002125645,0.00003544596,0.00005343417,0.004604806],"genre_scores_gemma":[0.9355302,0.00141901,0.05248504,0.0002141609,0.0001809476,0.0001770055,0.00007491359,0.00004488568,0.009873721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002570697,"threshold_uncertainty_score":0.01260555,"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."}}