{"id":"W2531556462","doi":"10.1016/j.ifacol.2016.07.351","title":"Bayesian Estimation in Stochastic Differential Equation Models via Laplace Approximation","year":2016,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Laplace's method; Laplace transform; Applied mathematics; Nonlinear system; Bayesian probability; Stochastic differential equation; Mathematics; Continuous stirred-tank reactor; Estimation theory; Bayes estimator; Noise (video); Mathematical optimization; Differential equation; Algorithm; Computer science; Statistics; Artificial intelligence; Mathematical analysis; Engineering","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":[0.00313514,0.0008568023,0.001330321,0.001217327,0.0004792824,0.001184845,0.001326231,0.00125724,0.001455224],"category_scores_gemma":[0.0133157,0.0008128432,0.001068641,0.001051759,0.001115224,0.001830234,0.001428354,0.002073013,0.0006214207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009563891,"about_ca_system_score_gemma":0.001374208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003695313,"about_ca_topic_score_gemma":0.002657,"domain_scores_codex":[0.9988391,0.0005245464,0.0000560595,0.0001778922,0.0003438701,0.00005859223],"domain_scores_gemma":[0.9947332,0.004312844,0.0002951513,0.0001496233,0.0004468734,0.00006236901],"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":[0.00005048858,0.00005160067,0.001048905,0.00008188386,0.0000624604,0.00007076462,0.000114679,0.8630617,0.001954712,0.05592914,0.0009513476,0.07662233],"study_design_scores_gemma":[0.000003487315,0.000006647109,0.00006335302,0.000004625329,0.000002513726,0.00001049972,0.000002813318,0.9900941,0.0002627513,0.009285464,0.0002581225,0.000005484616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00128673,0.00005220943,0.9983584,0.00004222157,0.000004160981,0.000007201922,0.000008971831,0.00006533483,0.0001747784],"genre_scores_gemma":[0.273261,0.0008762568,0.7213708,0.00020474,0.0001254746,0.0003283889,0.0003776041,0.0001665684,0.003289157],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003695313,"threshold_uncertainty_score":0.0165804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01084770452158224,"score_gpt":0.2123610172953886,"score_spread":0.2015133127738064,"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."}}