{"id":"W1855169468","doi":"10.1109/cdc.1998.758690","title":"Bank filters for ML parameter estimation via the expectation-maximization algorithm: the continuous-time case","year":2002,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; McGill University","funders":"","keywords":"Kalman filter; Expectation–maximization algorithm; Moment (physics); Fast Kalman filter; Conditional expectation; Estimation theory; Extended Kalman filter; Ensemble Kalman filter; Computer science; Algorithm; Maximization; Invariant extended Kalman filter; Stochastic process; Mathematics; Applied mathematics; Mathematical optimization; Maximum likelihood; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002798512,0.0001338036,0.0001107311,0.00004245399,0.0005577282,0.0003675022,0.0005298274,0.0000582928,0.0002726493],"category_scores_gemma":[0.000146658,0.00007490424,0.00007446911,0.0002537331,0.00005983647,0.0004536184,0.00007389791,0.00009885494,0.0001333897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001767295,"about_ca_system_score_gemma":0.000005862547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002896552,"about_ca_topic_score_gemma":0.000005091983,"domain_scores_codex":[0.9988948,0.0001079529,0.0002611668,0.0003020729,0.0002021592,0.0002319101],"domain_scores_gemma":[0.9977418,0.001328613,0.0001193056,0.0006501556,0.0001158341,0.0000442537],"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":[0.000004878111,0.00006625161,0.00002070509,0.000004580365,0.00002957806,0.00003488195,0.002584642,0.02596045,0.00003659712,0.002233474,0.1289944,0.8400296],"study_design_scores_gemma":[0.0002190185,0.00004249686,0.00002909403,0.000005612224,0.00001261373,0.0002689904,0.00008322786,0.9942999,0.0001733366,0.001344614,0.003399057,0.000122054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009643824,0.00005789265,0.994049,0.003447464,0.0004358872,0.00044987,0.00001086589,0.0002119761,0.0003727214],"genre_scores_gemma":[0.1977656,0.00001619719,0.7978584,0.002200577,0.0002244121,0.0001608033,0.00006025185,0.00002228123,0.001691562],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9683394,"threshold_uncertainty_score":0.428965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589082098759466,"score_gpt":0.2264326730418563,"score_spread":0.2105418520542617,"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."}}