{"id":"W2545991974","doi":"10.1109/nsspw.2006.4378829","title":"SMC Samplers for Bayesian Optimal Nonlinear Design","year":2006,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Software deployment; Monte Carlo method; Bayesian probability; Nonlinear system; Mathematical optimization; Bayesian experimental design; Bayesian network; Machine learning; Bayesian inference; Bayesian statistics; Artificial intelligence; Mathematics; Software engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004176128,0.0002181321,0.0003461933,0.0002320955,0.0001957071,0.0003556388,0.0007955991,0.0001105541,0.001825045],"category_scores_gemma":[0.001240828,0.00015971,0.0002280218,0.0005858156,0.0001352161,0.0003700666,0.00009375287,0.00008198679,0.0003450825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005892838,"about_ca_system_score_gemma":0.0000881466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000120775,"about_ca_topic_score_gemma":0.000008818522,"domain_scores_codex":[0.9968417,0.0003407354,0.0007170385,0.0006763425,0.000951009,0.0004731543],"domain_scores_gemma":[0.9952734,0.003736418,0.0001493671,0.0004637547,0.0002289775,0.0001480661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001110754,0.0008712325,0.001478338,0.00001115912,0.00006520523,0.00003039464,0.0004427195,0.07576691,0.3494013,0.06311375,0.3872394,0.1204688],"study_design_scores_gemma":[0.001628081,0.0007566039,0.0008570757,0.000006899577,0.00002130965,0.00002155499,0.0009121842,0.5345176,0.30879,0.0533081,0.09847537,0.0007052401],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002415373,0.00007817276,0.9786049,0.0003702518,0.0002497882,0.0006417815,0.00002206877,0.0001119447,0.01750573],"genre_scores_gemma":[0.0362156,7.8712e-7,0.9516937,0.0003429047,0.0002248576,0.00006313076,0.00000611158,0.00002973008,0.01142324],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4587507,"threshold_uncertainty_score":0.9990874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.217923709102971,"score_gpt":0.4595369197990576,"score_spread":0.2416132106960866,"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."}}