{"id":"W2083839279","doi":"10.1198/004017007000000038","title":"Incorporating Prior Information in Optimal Design for Model Selection","year":2007,"lang":"en","type":"article","venue":"Technometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Science Foundation","keywords":"Interpretability; Bayesian information criterion; Set (abstract data type); Selection (genetic algorithm); Computer science; Prior probability; Bayesian probability; Model selection; Hellinger distance; Machine learning; Identification (biology); Design of experiments; Mathematics; Mathematical optimization; Artificial intelligence; Data mining; 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":[0.04981189,0.003174748,0.004561552,0.004509161,0.00133686,0.002812553,0.002556118,0.002978294,0.004555586],"category_scores_gemma":[0.1563699,0.002120976,0.002505008,0.003343252,0.003350065,0.003891113,0.004000088,0.003936364,0.0007078727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00323583,"about_ca_system_score_gemma":0.005518917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002510866,"about_ca_topic_score_gemma":0.002858034,"domain_scores_codex":[0.9528067,0.04138959,0.0009971466,0.001618882,0.00268249,0.0005052302],"domain_scores_gemma":[0.8851153,0.1063901,0.002179686,0.003510951,0.002299384,0.0005046277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004468329,0.000178236,0.001334296,0.0005686012,0.0003865918,0.000158505,0.0002610582,0.6206293,0.0007491873,0.2691615,0.001266643,0.1048593],"study_design_scores_gemma":[0.0001638132,0.0001665123,0.000241015,0.000120588,0.00007950841,0.00002846159,0.00002598751,0.7469767,0.0005182485,0.2501027,0.001529152,0.0000472773],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001715795,0.0001842809,0.9971864,0.0001779957,0.00001718196,0.00009540268,0.00002791592,0.0001057394,0.000489261],"genre_scores_gemma":[0.1012451,0.0004580181,0.8958287,0.0002480549,0.00007162986,0.00137294,0.0001872243,0.00009223403,0.000496118],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04981189,"threshold_uncertainty_score":0.2634336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.214121415020881,"score_gpt":0.4495098550792087,"score_spread":0.2353884400583277,"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."}}