{"id":"W2088944695","doi":"10.1002/cjs.10037","title":"Bayesian optimal design for changepoint problems","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University Health Centre; McGill University","funders":"","keywords":"Optimal design; Bayesian probability; Measure (data warehouse); Bayesian experimental design; Computer science; Mathematical optimization; Statistics; Function (biology); Prior probability; Mathematics; Bayesian inference; Algorithm; Bayesian statistics; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00409407,0.0001624167,0.0003883322,0.000547545,0.0001939257,0.0003558271,0.000689378,0.00007859652,0.0003870255],"category_scores_gemma":[0.004365937,0.0001316942,0.0001090413,0.0003713966,0.0001153478,0.0002641297,0.000007510811,0.000177542,0.00002367973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002021323,"about_ca_system_score_gemma":0.001315382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001486601,"about_ca_topic_score_gemma":0.000428845,"domain_scores_codex":[0.9975208,0.0002531427,0.0009251848,0.0002110166,0.0006309277,0.000458946],"domain_scores_gemma":[0.9960555,0.001214477,0.0005393921,0.0002433085,0.0009097856,0.001037567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003082058,0.0001101284,0.0003576642,0.00001775656,0.00008950764,0.0009127982,0.005532208,0.03787367,0.006303652,0.05008465,0.4176392,0.4807706],"study_design_scores_gemma":[0.003151024,0.01095934,0.004216058,0.0001916753,0.0001457132,0.001311454,0.0026575,0.1296913,0.01071817,0.7118255,0.1239556,0.001176618],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003407876,0.0004246659,0.9966205,0.0009011946,0.0005765063,0.0003197574,0.0002026557,0.000004178651,0.0006097895],"genre_scores_gemma":[0.1365479,0.000006780205,0.8622815,0.0004906264,0.0001822176,0.000003423727,0.000002279974,0.00001532724,0.0004699683],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6617409,"threshold_uncertainty_score":0.5370333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2133182137500489,"score_gpt":0.4045466788676051,"score_spread":0.1912284651175562,"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."}}