{"id":"W2991569184","doi":"10.3390/math7121143","title":"Variational Bayesian Iterative Estimation Algorithm for Linear Difference Equation Systems","year":2019,"lang":"en","type":"article","venue":"Mathematics","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Discretization; Mathematics; Applied mathematics; Kalman filter; Differential equation; Partial differential equation; Basis (linear algebra); Iterative method; Algorithm; Mathematical optimization; Computer science; Mathematical analysis; 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":[0.002149939,0.0008622158,0.001519539,0.0007995603,0.0005917287,0.001213,0.002004822,0.00169748,0.003007155],"category_scores_gemma":[0.006395383,0.0008306005,0.001047182,0.001043752,0.001036432,0.001365503,0.002063012,0.002281696,0.0006210388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416665,"about_ca_system_score_gemma":0.002490786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01081472,"about_ca_topic_score_gemma":0.00844041,"domain_scores_codex":[0.9989653,0.000414162,0.00005487916,0.0001774216,0.0003116948,0.00007661493],"domain_scores_gemma":[0.9981184,0.001319006,0.0001216947,0.0000546122,0.0003377587,0.00004848544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006206521,0.00003640663,0.0006609597,0.0001699187,0.00008669697,0.00007226243,0.0001598743,0.8175408,0.001858722,0.09096523,0.001837128,0.08654995],"study_design_scores_gemma":[0.000005862738,0.000007998845,0.00004731646,0.000006966397,0.000004475552,0.00001088205,0.000004086065,0.9896818,0.0001952274,0.009295,0.0007345324,0.000005858121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008430363,0.0001534487,0.9982147,0.00008108889,0.00001436009,0.00002053408,0.00001673148,0.00005958054,0.0005964498],"genre_scores_gemma":[0.1796454,0.000778209,0.812453,0.0002231742,0.0001004994,0.0005847111,0.0004350474,0.0001953335,0.005584631],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01081472,"threshold_uncertainty_score":0.02150357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02501073467306784,"score_gpt":0.2600874498703694,"score_spread":0.2350767151973015,"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."}}