{"id":"W1985862691","doi":"10.1002/cjce.20557","title":"Parameter estimation in models with hidden variables : An application to a biotech process","year":2011,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Control Systems and Identification","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia","funders":"","keywords":"Gibbs sampling; Sampling (signal processing); Bayesian probability; Nonlinear system; Process (computing); Stochastic process; Mathematics; Computer science; Estimation theory; Metropolis–Hastings algorithm; Algorithm; Set (abstract data type); Mathematical optimization; Artificial intelligence; Statistics; Markov chain Monte Carlo","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.004966402,0.0008539078,0.001224836,0.0007754047,0.0009391284,0.001411681,0.001189754,0.002222,0.001532133],"category_scores_gemma":[0.01620866,0.0006159607,0.0009769258,0.001131457,0.001369185,0.001098115,0.001706171,0.002135858,0.0002320645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001211306,"about_ca_system_score_gemma":0.001427673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02195093,"about_ca_topic_score_gemma":0.01293925,"domain_scores_codex":[0.9989296,0.0006218488,0.00004276438,0.0001554964,0.000186652,0.00006371394],"domain_scores_gemma":[0.9882517,0.01054074,0.0004391573,0.0002278618,0.0004439229,0.00009659702],"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":[0.00006472397,0.00004874773,0.001135512,0.00004502129,0.00003905613,0.0001223341,0.0001220013,0.9704377,0.0005189674,0.01438804,0.0001837919,0.01289419],"study_design_scores_gemma":[0.000004872307,0.000005989384,0.00008356095,0.000002772355,0.000002771691,0.000006100958,0.000004521162,0.9956723,0.000113559,0.004024192,0.00007504912,0.000004267946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06193051,0.000386197,0.9359607,0.0005653364,0.00002391216,0.00004120829,0.00006050292,0.0001827753,0.0008488247],"genre_scores_gemma":[0.8306111,0.0004522023,0.1661809,0.00009247596,0.00005754807,0.0001326094,0.0001619611,0.00006170975,0.002249554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02195093,"threshold_uncertainty_score":0.04364634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01046181038874812,"score_gpt":0.1812168439554037,"score_spread":0.1707550335666556,"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."}}