{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003449828,0.0001272582,0.0002282566,0.0001993745,0.00006504237,0.0001524193,0.0001644072,0.00004410819,0.0000044389],"category_scores_gemma":[0.00003269382,0.0001078266,0.00007446754,0.0001074521,0.00003080105,0.0005227246,0.00001119689,0.00014712,0.000002356155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005681665,"about_ca_system_score_gemma":0.00005943742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004790357,"about_ca_topic_score_gemma":0.000002851171,"domain_scores_codex":[0.9990484,0.00002295127,0.0003639927,0.0001041034,0.0002859379,0.000174673],"domain_scores_gemma":[0.99913,0.0001096941,0.000174539,0.00003168784,0.0004700354,0.00008411161],"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":[0.0009071998,0.0000316234,0.00009893175,0.0001146952,0.0005568788,0.00006926277,0.001269923,0.7193542,0.04537929,0.002617716,0.0003329873,0.2292673],"study_design_scores_gemma":[0.001867469,0.00009646685,0.000167422,0.000170772,0.0000183776,0.00004463977,0.0002503096,0.9864042,0.0003900904,0.009600701,0.0008686583,0.0001208475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0119328,0.00094409,0.9855445,0.0004134958,0.0004054612,0.0001390525,0.00003976831,0.00008423503,0.0004966001],"genre_scores_gemma":[0.9987718,0.00007567697,0.0006442264,0.00006670391,0.0002798937,0.00001832515,0.000001625819,0.0000188395,0.0001229034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.986839,"threshold_uncertainty_score":0.439704,"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."}}