{"id":"W2793406240","doi":"10.1002/sam.11371","title":"Informative priors in Bayesian inference and computation","year":2018,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Prior probability; Inference; Computer science; Bayesian inference; Prior information; Bayesian probability; Machine learning; Artificial intelligence; Computation; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.03017854,0.002408907,0.003530806,0.004646839,0.001670378,0.007232322,0.004007127,0.005737203,0.006355083],"category_scores_gemma":[0.1326951,0.002059319,0.002096474,0.007163701,0.01205022,0.01097842,0.004980169,0.01057083,0.002326977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004213633,"about_ca_system_score_gemma":0.004251957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005736412,"about_ca_topic_score_gemma":0.00410517,"domain_scores_codex":[0.9719523,0.02055774,0.001065756,0.001979607,0.004024893,0.0004196665],"domain_scores_gemma":[0.8945181,0.09573204,0.002036428,0.0044093,0.00272221,0.0005820078],"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":[0.00002749879,0.00002540653,0.0003402973,0.0003654783,0.0001046094,0.00008225329,0.0001759576,0.03371439,0.00009446075,0.9110118,0.004645335,0.0494126],"study_design_scores_gemma":[0.000009739674,0.000006201929,0.00005173326,0.00008804355,0.00001256552,0.00002476457,0.00001748975,0.02888506,0.00007887816,0.9664587,0.004351213,0.00001561701],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005893672,0.005873165,0.9850768,0.002973749,0.0002436986,0.00004811018,0.00009182782,0.0001542433,0.004948938],"genre_scores_gemma":[0.107264,0.01918289,0.8630629,0.002290745,0.002545733,0.0007952438,0.0003904993,0.0004087269,0.004059244],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03017854,"threshold_uncertainty_score":0.1596012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05260797491513945,"score_gpt":0.3710747324231752,"score_spread":0.3184667575080358,"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."}}