{"id":"W4385723988","doi":"10.1002/acs.3667","title":"Approximate Gaussian variance inference for state‐space models","year":2023,"lang":"en","type":"article","venue":"International Journal of Adaptive Control and Signal Processing","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Institut de Valorisation des Données; Polytechnique Montréal","keywords":"Univariate; Kalman filter; Inference; Covariance matrix; State space; Gaussian process; Variance (accounting); Computer science; Algorithm; Gaussian; State-space representation; Covariance; Bayesian inference; Bayesian probability; Mathematics; Multivariate statistics; Statistics; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003258684,0.0007229112,0.00106995,0.00102873,0.000490407,0.001275231,0.001652534,0.001105095,0.001707184],"category_scores_gemma":[0.0168169,0.0007194997,0.0009510498,0.001011599,0.001174214,0.001845652,0.001115548,0.002317309,0.0004554804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001321908,"about_ca_system_score_gemma":0.002009571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01211047,"about_ca_topic_score_gemma":0.01155985,"domain_scores_codex":[0.9982843,0.0005982186,0.0000687505,0.0003018354,0.0005999354,0.0001469389],"domain_scores_gemma":[0.9931171,0.005167189,0.0004439797,0.0005680662,0.0006291429,0.00007450206],"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":[0.00004874911,0.00003046237,0.0007339254,0.00003962817,0.00003760414,0.00003911203,0.00004079411,0.9219462,0.0008888746,0.04430605,0.0007339426,0.03115467],"study_design_scores_gemma":[0.000002214732,0.000003184431,0.00009746678,0.000003570137,0.000002032876,0.000005323802,0.000002586118,0.9860875,0.0001902509,0.01347613,0.000125909,0.000003813443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003723413,0.00006001972,0.9954882,0.00006114563,0.000008259984,0.000007402111,0.00003870692,0.0001453946,0.0004674225],"genre_scores_gemma":[0.6705557,0.0004698248,0.3238211,0.0001974891,0.0001005246,0.0001388006,0.0006577951,0.0002094033,0.00384939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01211047,"threshold_uncertainty_score":0.02407998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01852336259352092,"score_gpt":0.2575324710625029,"score_spread":0.239009108468982,"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."}}