{"id":"W3113374883","doi":"","title":"Interpretable Bayesian Functional Linear Regression","year":2015,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bayesian probability; Linear regression; Bayesian multivariate linear regression; Artificial intelligence; Bayesian linear regression; Regression; Machine learning; Computer science; Regression analysis; Mathematics; Statistics; Pattern recognition (psychology); Bayesian inference","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008157086,0.001452324,0.001385587,0.001972464,0.000526785,0.002708503,0.001950218,0.002678436,0.01272913],"category_scores_gemma":[0.0326697,0.001001874,0.001816411,0.001305794,0.001825151,0.003208354,0.00241854,0.003699028,0.002594981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001910105,"about_ca_system_score_gemma":0.001370784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004233818,"about_ca_topic_score_gemma":0.004349246,"domain_scores_codex":[0.9946653,0.003597662,0.0001894005,0.0005905058,0.0007658357,0.0001913705],"domain_scores_gemma":[0.9899727,0.00636574,0.0006794521,0.001205969,0.001575753,0.0002004539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001534853,0.00004865848,0.001017412,0.0003339743,0.0001286308,0.0002419942,0.0002564859,0.111066,0.002193878,0.7578422,0.01854955,0.1081677],"study_design_scores_gemma":[0.00002236972,0.00001932724,0.0004800983,0.0001001398,0.00003355765,0.000102208,0.00003095045,0.3342357,0.0005609723,0.6541017,0.01028415,0.00002875319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002500096,0.0008160605,0.9896645,0.001294149,0.0001174915,0.0000242139,0.0005553634,0.000401803,0.004626272],"genre_scores_gemma":[0.321796,0.0024254,0.6424119,0.002001363,0.00101462,0.0004632759,0.004155756,0.00163417,0.02409748],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01272913,"threshold_uncertainty_score":0.04313934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03312253628124889,"score_gpt":0.2652861271825114,"score_spread":0.2321635909012625,"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."}}