{"id":"W2615536673","doi":"10.1109/tac.2017.2704442","title":"Variational Bayesian Adaptive Cubature Information Filter Based on Wishart Distribution","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Wishart distribution; Inverse-Wishart distribution; Mathematics; Recursive Bayesian estimation; Posterior probability; Bayesian probability; Noise (video); Algorithm; Computer science; Artificial intelligence; Statistics; Multivariate 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":[0.00201409,0.0007957815,0.00168611,0.0008465673,0.0007276505,0.00172882,0.001798593,0.001507344,0.002631949],"category_scores_gemma":[0.004715324,0.0006984876,0.001125219,0.001154134,0.0009746686,0.002664894,0.001278658,0.001544617,0.0007293306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001467976,"about_ca_system_score_gemma":0.001782094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009480831,"about_ca_topic_score_gemma":0.00522154,"domain_scores_codex":[0.9987774,0.0003840725,0.00006215546,0.0002593731,0.0004032549,0.0001137012],"domain_scores_gemma":[0.9984818,0.000795626,0.0001149954,0.00009984559,0.0004474073,0.00006032044],"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":[0.0002790348,0.00007491196,0.001363317,0.0001997321,0.0001732083,0.0001452174,0.0002419887,0.6195301,0.0104543,0.1867023,0.004654844,0.176181],"study_design_scores_gemma":[0.000007824427,0.00001545591,0.00007363682,0.000005356123,0.000009388103,0.00002468892,0.000004725126,0.9904536,0.0009831033,0.007485153,0.0009227156,0.00001441374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00140648,0.0001129421,0.9978145,0.00006229067,0.00001586306,0.000009609499,0.00001943201,0.00008507542,0.0004737878],"genre_scores_gemma":[0.3200743,0.001243241,0.6666797,0.0005366553,0.0001878256,0.0003340488,0.0006690927,0.0003063108,0.009968739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009480831,"threshold_uncertainty_score":0.01885128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00969140815505092,"score_gpt":0.2236731113282009,"score_spread":0.21398170317315,"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."}}