{"id":"W2067883763","doi":"10.1111/j.1541-0420.2008.01013.x","title":"Bayesian Estimation of Inverse Dose Response","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Maximum a posteriori estimation; Posterior probability; Bayesian probability; Bayesian inference; Mathematics; Posterior predictive distribution; Statistics; Prior probability; A priori and a posteriori; Computer science; Bayes estimator; Inverse problem; Bayesian linear regression; Algorithm; Maximum likelihood","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01855566,0.001177651,0.002353009,0.001796905,0.0005126931,0.00165536,0.001985471,0.0020404,0.003823713],"category_scores_gemma":[0.05935592,0.0007530068,0.001624826,0.001242651,0.001832348,0.001882997,0.001521372,0.002464049,0.0008004103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001884738,"about_ca_system_score_gemma":0.001857601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002739919,"about_ca_topic_score_gemma":0.002003568,"domain_scores_codex":[0.9872722,0.008741596,0.0004069782,0.001509766,0.00185148,0.0002179403],"domain_scores_gemma":[0.9757904,0.02054246,0.001279803,0.000958166,0.001269654,0.0001594336],"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":[0.0006733009,0.0002310511,0.006058888,0.0007077099,0.0004904277,0.0001613009,0.0002608347,0.6749916,0.00414086,0.1194201,0.00342125,0.1894427],"study_design_scores_gemma":[0.0001290449,0.0002145154,0.003095168,0.0001576886,0.0001632171,0.0001924777,0.00003554358,0.8691931,0.002884633,0.1202023,0.003644736,0.00008754397],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006940224,0.0004622901,0.9904572,0.0003205407,0.00002361216,0.0001351191,0.00009516979,0.0001057783,0.001460148],"genre_scores_gemma":[0.3528232,0.001005334,0.6413636,0.000485729,0.00009307671,0.001125502,0.0005046442,0.00008193796,0.00251696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01855566,"threshold_uncertainty_score":0.09813285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.262967311778844,"score_gpt":0.4629643026311734,"score_spread":0.1999969908523295,"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."}}