{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004011854,0.0009664678,0.002415687,0.001697376,0.0009141284,0.001468874,0.001634767,0.001435664,0.001516194],"category_scores_gemma":[0.01959513,0.00121403,0.00143565,0.0008756074,0.001141129,0.001533313,0.002316792,0.00202899,0.0005683172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007964628,"about_ca_system_score_gemma":0.001674443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003750914,"about_ca_topic_score_gemma":0.003859725,"domain_scores_codex":[0.9971098,0.001600725,0.0001452677,0.000441988,0.0005639618,0.0001381894],"domain_scores_gemma":[0.9909814,0.0073412,0.0004286266,0.0004053339,0.0007023782,0.0001410823],"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":[0.0003468263,0.000164776,0.0009922972,0.0003166228,0.0003048863,0.0002103163,0.0001657927,0.7831787,0.005853136,0.02961896,0.002218534,0.1766292],"study_design_scores_gemma":[0.00001272916,0.00001758513,0.000137545,0.000005288868,0.00001300257,0.00001713268,0.000002989998,0.9865999,0.0004536007,0.01254741,0.0001811382,0.00001158895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007942521,0.0002552708,0.9910086,0.0001358751,0.00001995022,0.00002564778,0.00002665117,0.0002238154,0.0003616417],"genre_scores_gemma":[0.6745859,0.0005101264,0.3206139,0.0001982271,0.0002236864,0.0002527749,0.0004169552,0.0002739558,0.002924503],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004011854,"threshold_uncertainty_score":0.02121699,"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."}}