{"id":"W4390618520","doi":"10.1080/10618600.2024.2302528","title":"A Bayesian Collocation Integral Method for Parameter Estimation in Ordinary Differential Equations","year":2024,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Discovery Eye Foundation","keywords":"Ode; Ordinary differential equation; Collocation (remote sensing); Collocation method; Applied mathematics; Nonlinear system; Bayesian probability; Computer science; Orthogonal collocation; Mathematics; Mathematical optimization; Bayes estimator; Estimation theory; Gaussian; Basis (linear algebra); Numerical integration; Algorithm; Differential equation; Mathematical analysis; 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":[0.003139503,0.0009623157,0.001415254,0.001151485,0.0006645937,0.0009330933,0.001760136,0.001835431,0.002920421],"category_scores_gemma":[0.008888192,0.0007374349,0.001123006,0.001181554,0.001174682,0.001237807,0.001729882,0.002424799,0.0008670358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006001,"about_ca_system_score_gemma":0.00216304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009744392,"about_ca_topic_score_gemma":0.008518134,"domain_scores_codex":[0.9990854,0.0004042149,0.00004654103,0.000132736,0.0002824955,0.00004860087],"domain_scores_gemma":[0.9971371,0.002009126,0.0002226277,0.0001370735,0.0004059724,0.00008813458],"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":[0.0001115264,0.00006135353,0.001131666,0.0002805086,0.00014576,0.000158426,0.0001910757,0.7992828,0.004713239,0.1035851,0.003348351,0.08699021],"study_design_scores_gemma":[0.000006321558,0.000007548921,0.00007252229,0.00001607297,0.000007186084,0.00001372698,0.000005105201,0.9874665,0.0002649395,0.01087299,0.001255563,0.00001150295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007306584,0.0001656536,0.9986489,0.00005895597,0.0000177341,0.00001269259,0.00002680449,0.00007702166,0.0002616137],"genre_scores_gemma":[0.1112223,0.001059838,0.8831193,0.0002719073,0.0001377633,0.0003304778,0.0004013271,0.0003397681,0.003117276],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009744392,"threshold_uncertainty_score":0.01937538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01458855396392059,"score_gpt":0.3136028009829331,"score_spread":0.2990142470190125,"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."}}