{"id":"W2999823193","doi":"10.1016/j.idm.2019.12.007","title":"Bayesian inference for dynamical systems","year":2020,"lang":"en","type":"article","venue":"Infectious Disease Modelling","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Markov chain Monte Carlo; Bayesian inference; Inference; Bayesian probability; Fiducial inference; Bayesian statistics; Computer science; Statistical inference; Frequentist inference; Mathematics; Machine learning; Data mining; Algorithm; Artificial intelligence; Statistics","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":[0.00009764593,0.0002295118,0.0002403234,0.00007054296,0.0002297051,0.0005383966,0.000656782,0.00007151874,0.000006522787],"category_scores_gemma":[0.00009295929,0.0002211812,0.0001313229,0.000375108,0.00003644903,0.0005670417,0.0001347936,0.0001575662,0.0000416818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004695735,"about_ca_system_score_gemma":0.0002331197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002528443,"about_ca_topic_score_gemma":0.000001433947,"domain_scores_codex":[0.9983369,0.00003676262,0.0003235306,0.0006333234,0.0002487889,0.0004206704],"domain_scores_gemma":[0.9985877,0.0001300564,0.0001114178,0.0003779026,0.0001682441,0.0006246962],"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":[0.00002638572,0.00007968742,0.001532513,0.0003797138,0.00002621542,0.00002201681,0.000310802,0.6785889,0.00002890993,0.3159399,0.0001373132,0.002927617],"study_design_scores_gemma":[0.0002919503,0.00008709194,0.00003716953,0.00004997192,0.00001925815,0.000003229947,0.000007150964,0.968539,0.00001528587,0.0299057,0.0007573708,0.0002868218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002337923,0.0002567706,0.9942909,0.001191062,0.0002918682,0.000424666,0.0000200038,0.0005733227,0.0006134795],"genre_scores_gemma":[0.9863327,0.00002528272,0.01261769,0.0006426304,0.0002037857,0.0001267856,0.000008410444,0.00002191886,0.00002075473],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9839948,"threshold_uncertainty_score":0.9019505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02568718585408232,"score_gpt":0.2502482133426714,"score_spread":0.2245610274885891,"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."}}