{"id":"W2125945227","doi":"10.1002/cjce.20113","title":"A particle filter approach to identification of nonlinear processes under missing observations","year":2008,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Control Systems and Identification","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Missing data; Expectation–maximization algorithm; Likelihood function; Particle filter; Maximization; Nonlinear system; Mathematics; Algorithm; Path (computing); Identification (biology); Function (biology); Mathematical optimization; Filter (signal processing); Applied mathematics; Estimation theory; Computer science; Statistics; Maximum likelihood; Kalman filter","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001658138,0.00007025388,0.0001278227,0.00007543877,0.0000480738,0.00003352938,0.0001696101,0.00003581263,0.000003147105],"category_scores_gemma":[0.0001879061,0.00005933832,0.00004274527,0.000301548,0.00002123177,0.0001265034,0.000003782138,0.0001109561,0.000002334001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009350742,"about_ca_system_score_gemma":0.0001402393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001952982,"about_ca_topic_score_gemma":0.00005317036,"domain_scores_codex":[0.9992853,0.000005511592,0.0003919977,0.00005171744,0.0001229214,0.0001425173],"domain_scores_gemma":[0.9994065,0.00003720699,0.00006521858,0.0001265546,0.0001870064,0.0001774823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002244933,0.000008763333,0.0001561014,0.0001017677,0.0000366196,0.00000129136,0.0009546942,0.3574614,0.6406229,0.0001667765,0.000288407,0.0001990026],"study_design_scores_gemma":[0.0003402041,0.00001367636,0.006364268,0.0001591162,0.00005265123,0.0001753711,0.00007592009,0.4512456,0.5395865,0.0000842919,0.001664359,0.0002380276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9666145,0.0005093474,0.03221649,0.0003531729,0.0001414713,0.00009363436,0.000006228463,0.00001781041,0.00004738143],"genre_scores_gemma":[0.998745,0.000002343679,0.001056301,0.00001673659,0.0001316303,0.000005205779,0.000002331687,0.00001598025,0.00002441986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1010364,"threshold_uncertainty_score":0.2419747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03049308831613182,"score_gpt":0.1961360334235104,"score_spread":0.1656429451073786,"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."}}