{"id":"W2587831136","doi":"10.1016/j.cma.2017.01.042","title":"Bayesian model selection using automatic relevance determination for nonlinear dynamical systems","year":2017,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ontario Innovation Trust","keywords":"Mathematics; Parameter space; Model selection; Nonlinear system; Maximum a posteriori estimation; Applied mathematics; Prior probability; Bayesian inference; Markov chain Monte Carlo; Mathematical optimization; Bayesian probability; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003028855,0.0001764785,0.0003544302,0.0002128329,0.0001931532,0.0003505393,0.0003893216,0.0001401414,5.734416e-7],"category_scores_gemma":[0.0007809883,0.0001607713,0.00004623138,0.0001322931,0.00001054238,0.0001330566,0.0001317785,0.0001567178,4.613875e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009317451,"about_ca_system_score_gemma":0.0000284921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000236498,"about_ca_topic_score_gemma":7.188531e-7,"domain_scores_codex":[0.9986111,0.00003545899,0.0004741152,0.0004200256,0.0001997581,0.0002595309],"domain_scores_gemma":[0.9985111,0.0007785147,0.0001648234,0.0003942107,0.00007045433,0.00008094227],"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":[0.00000365,0.000006191565,0.000002490247,0.00006921036,0.00000454978,4.925893e-7,0.00004552051,0.8673667,0.004679002,0.04197608,0.000002748511,0.08584335],"study_design_scores_gemma":[0.0002554732,0.00001815398,0.00001786381,0.00006827748,0.00001117701,0.00001046983,0.000007301057,0.9715415,0.0001598883,0.02767166,0.0000544786,0.0001838081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001838204,0.00004294531,0.9969112,0.00001097442,0.0007209209,0.0003783974,0.000003638756,0.00007750932,0.00001623094],"genre_scores_gemma":[0.1618382,0.000005175737,0.8379694,0.000005782504,0.0001079678,0.00004144211,9.689248e-7,0.00002322871,0.000007867231],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.16,"threshold_uncertainty_score":0.6556063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09805260451440018,"score_gpt":0.3906018728854854,"score_spread":0.2925492683710852,"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."}}