{"id":"W2340010629","doi":"10.1002/cjs.11283","title":"Bayesian inference for high‐dimensional linear regression under mnet priors","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Prior probability; Hyperparameter; Computer science; Markov chain Monte Carlo; Bayesian inference; Inference; Bayesian probability; Model selection; Bayesian linear regression; Posterior probability; Machine learning; Statistical inference; Artificial intelligence; Data mining; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01773105,0.001131443,0.002306736,0.002045286,0.00098691,0.002029303,0.002673078,0.001857097,0.003742883],"category_scores_gemma":[0.06363446,0.001353741,0.001818297,0.002521077,0.002460712,0.002584405,0.001996368,0.003850433,0.0007468822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002437719,"about_ca_system_score_gemma":0.002205257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01445458,"about_ca_topic_score_gemma":0.01213691,"domain_scores_codex":[0.9928129,0.005354036,0.0002248501,0.0007276118,0.0006844168,0.0001961572],"domain_scores_gemma":[0.9537892,0.04131877,0.001530114,0.001519964,0.001526401,0.0003156442],"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":[0.0001554512,0.00004700346,0.002379831,0.0001261448,0.0001660968,0.0001030229,0.00008457527,0.8410347,0.000396387,0.1176487,0.002087753,0.03577047],"study_design_scores_gemma":[0.00001395336,0.000009101865,0.0002189445,0.0000157381,0.000009505526,0.00001409431,0.000006240336,0.9454813,0.0001392501,0.05363173,0.0004515008,0.000008668633],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004793243,0.0001896871,0.9941308,0.0002333781,0.00002112285,0.00002566144,0.0001177597,0.0001335761,0.0003546164],"genre_scores_gemma":[0.4157647,0.00119649,0.5750213,0.0004817575,0.0003264772,0.0006696393,0.001636353,0.0002787059,0.004624534],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01773105,"threshold_uncertainty_score":0.09377193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08895249818211964,"score_gpt":0.356384200709832,"score_spread":0.2674317025277123,"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."}}