{"id":"W2344934955","doi":"10.1016/j.jprocont.2016.04.003","title":"Robust Gaussian process modeling using EM algorithm","year":2016,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Health Solutions","keywords":"Gaussian process; Robust regression; Conjugate gradient method; Algorithm; Stability (learning theory); Kriging; Regression; Computer science; Marginal likelihood; Bayesian linear regression; Convergence (economics); Mathematical optimization; Regression analysis; Mathematics; Gaussian; Bayesian probability; Bayesian inference; Machine learning; Artificial intelligence; Statistics","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":[0.003666166,0.001466396,0.002665677,0.001308362,0.0007358873,0.00167387,0.002603522,0.002708178,0.003287064],"category_scores_gemma":[0.01482637,0.001608342,0.002380711,0.00177117,0.001204984,0.002784769,0.002452679,0.003484096,0.001924664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007643925,"about_ca_system_score_gemma":0.002024689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005557591,"about_ca_topic_score_gemma":0.003464195,"domain_scores_codex":[0.9982641,0.0007490561,0.0001108006,0.0004120139,0.0003663284,0.00009769822],"domain_scores_gemma":[0.9952245,0.003308473,0.0003347335,0.0004370392,0.0006258603,0.00006943605],"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":[0.0001393227,0.00007528572,0.000440961,0.0001459507,0.0002531815,0.00008737604,0.00007782815,0.8213937,0.001546673,0.06758283,0.002189515,0.1060674],"study_design_scores_gemma":[0.000009588624,0.000008150902,0.00005102414,0.000008010358,0.00001606497,0.00001686233,0.000003097932,0.9812238,0.000392598,0.01766112,0.0005993873,0.00001027401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005325916,0.00006116838,0.9989339,0.00004175528,0.00001302159,0.000007428582,0.00001619444,0.0001705804,0.000223405],"genre_scores_gemma":[0.2001255,0.0005888113,0.792434,0.0002212239,0.0001468002,0.0002823176,0.0005879211,0.00051187,0.005101497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005557591,"threshold_uncertainty_score":0.01938874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02819780691223422,"score_gpt":0.2661950150392292,"score_spread":0.237997208126995,"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."}}