{"id":"W2564136532","doi":"10.1109/dsaa.2016.67","title":"Informative Priors and Bayesian Computation","year":2016,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Prior probability; Computer science; Prior information; Inference; Bayesian probability; Bayesian inference; Machine learning; Computation; Artificial intelligence; Algorithm","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":[0.01467978,0.001507358,0.002067739,0.00402746,0.001447168,0.005584232,0.00284766,0.003903672,0.006497534],"category_scores_gemma":[0.07913717,0.001286689,0.001427503,0.004158949,0.008940096,0.007212536,0.003155137,0.006560843,0.001760667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003578371,"about_ca_system_score_gemma":0.002918003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005073548,"about_ca_topic_score_gemma":0.003557898,"domain_scores_codex":[0.9872288,0.008855095,0.0004059901,0.001182504,0.002027085,0.0003006294],"domain_scores_gemma":[0.9614239,0.03392736,0.001141866,0.001830487,0.001313969,0.0003624008],"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":[0.00001500474,0.00001305959,0.0002124464,0.0001599684,0.00004562658,0.00005139963,0.0001262789,0.02976188,0.0000811276,0.9383223,0.002362011,0.02884877],"study_design_scores_gemma":[0.000005440063,0.000003524572,0.00005417646,0.00004889398,0.000006391976,0.00002113124,0.00001283932,0.02273265,0.00005455802,0.973922,0.003127992,0.00001045375],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001388938,0.003539535,0.9810708,0.003258,0.0001622431,0.00004370288,0.0001120637,0.0001586695,0.01026614],"genre_scores_gemma":[0.2521417,0.01367765,0.7221589,0.002420185,0.001683297,0.0006160494,0.0004865713,0.0003810395,0.006434601],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01467978,"threshold_uncertainty_score":0.07763505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006763566499875171,"score_gpt":0.2217822479665868,"score_spread":0.2150186814667117,"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."}}