{"id":"W938483330","doi":"10.1007/s11071-015-2217-8","title":"Bayesian parameter estimation and model selection for strongly nonlinear dynamical systems","year":2015,"lang":"en","type":"article","venue":"Nonlinear Dynamics","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada; Carleton University","funders":"Else Kröner-Fresenius-Stiftung","keywords":"Markov chain Monte Carlo; Particle filter; Nonlinear system; Bayesian probability; Bayesian inference; Mathematics; Model selection; Kalman filter; Ensemble Kalman filter; Applied mathematics; Posterior probability; Monte Carlo method; Estimation theory; Gaussian; Metropolis–Hastings algorithm; Algorithm; Statistical physics; Extended Kalman filter; Statistics; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003857742,0.0001975964,0.0002289846,0.0001037423,0.0001384883,0.000320905,0.0003399122,0.0001948976,4.674771e-7],"category_scores_gemma":[0.0001550284,0.0001889465,0.00005275564,0.0002157504,0.00004712936,0.0004606591,0.0001227622,0.0002171967,0.000006497409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001360818,"about_ca_system_score_gemma":0.000110898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004182447,"about_ca_topic_score_gemma":0.00006828762,"domain_scores_codex":[0.9985057,0.00005253303,0.0003374919,0.0004795098,0.000289389,0.0003353977],"domain_scores_gemma":[0.9988764,0.0001686312,0.0001212211,0.0003805349,0.000235498,0.0002177328],"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":[0.00003447163,0.00008042646,0.0005516378,0.00003366014,0.00001575121,0.000001375483,0.0001069183,0.9594329,0.00001060741,0.01622328,0.0004290822,0.02307989],"study_design_scores_gemma":[0.0005258641,0.0001352322,0.00002226897,0.000022885,0.00001634441,0.00003881373,0.00002845068,0.9967095,0.000004999005,0.001880177,0.0003966823,0.0002188233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02653687,0.00004498718,0.9718007,0.0002397769,0.0005792794,0.0003352923,0.0001126819,0.0002719037,0.00007845998],"genre_scores_gemma":[0.1939517,0.000007555143,0.8052786,0.00005132942,0.0001796592,0.00002304509,0.0003318072,0.00002542402,0.000150903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1674148,"threshold_uncertainty_score":0.7705016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02282818038570092,"score_gpt":0.2700032135954024,"score_spread":0.2471750332097015,"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."}}