{"id":"W2964086572","doi":"","title":"Robust and Trend-following Kalman Smoothers using Student’s t","year":2016,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Kalman filter; Maximum a posteriori estimation; Solver; Estimator; Student's t-distribution; Noise (video); Nonlinear system; Algorithm; Computer science; Gaussian; Mathematical optimization; Mathematics; Artificial intelligence; Maximum likelihood; Statistics; Image (mathematics); Econometrics","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":[0.0001931125,0.0001121308,0.0001175464,0.0000697419,0.000146496,0.0001559018,0.0004325229,0.00004729807,0.00005230362],"category_scores_gemma":[0.00001076769,0.00006914571,0.00004784338,0.0001527967,0.00003213316,0.0004619549,0.0002539748,0.00004899606,0.00001667673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002087215,"about_ca_system_score_gemma":0.00001045876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003474681,"about_ca_topic_score_gemma":0.00002400959,"domain_scores_codex":[0.9989926,0.000040311,0.0001527189,0.0003580259,0.0002023109,0.0002540639],"domain_scores_gemma":[0.9993306,0.0001105426,0.00003867353,0.0004106065,0.00001073707,0.00009888926],"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":[0.00001756321,0.000250473,0.1407005,0.00001734357,0.0002087642,0.0002601182,0.002483899,0.002261663,0.02066027,0.09652603,0.02046638,0.716147],"study_design_scores_gemma":[0.01682807,0.0008519046,0.1930562,0.001863423,0.0002706451,0.0006276409,0.002021354,0.4464732,0.0102854,0.0124585,0.3081572,0.007106389],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2958722,0.0001481753,0.6983353,0.0005927522,0.0007893979,0.00005915087,0.000001989633,0.0002901335,0.003910837],"genre_scores_gemma":[0.9192851,0.00002800267,0.07922494,0.0002932534,0.00009276919,0.000001397344,5.751114e-7,0.00001157215,0.00106246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7090406,"threshold_uncertainty_score":0.281968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04371152105290188,"score_gpt":0.2691898916459473,"score_spread":0.2254783705930454,"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."}}