{"id":"W2968490943","doi":"10.1016/j.sste.2019.100301","title":"Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan","year":2019,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Traffic and Road Safety","field":"Engineering","cited_by":184,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development","keywords":"Markov chain Monte Carlo; Bayesian inference; Bayesian probability; Census tract; Census; Inference; Monte Carlo method; Computer science; Pedestrian; Statistics; Cartography; Geography; Econometrics; Artificial intelligence; Mathematics; Medicine; Environmental health","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":[0.004911892,0.000519365,0.001620612,0.001694363,0.00101185,0.002307935,0.002807239,0.001681024,0.008440274],"category_scores_gemma":[0.01578129,0.00123574,0.001246459,0.001774665,0.0009422443,0.002732957,0.002672242,0.001873012,0.001609288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001844108,"about_ca_system_score_gemma":0.003126262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06606711,"about_ca_topic_score_gemma":0.07693606,"domain_scores_codex":[0.9988787,0.000716028,0.00006749513,0.0001205255,0.0001361869,0.00008098411],"domain_scores_gemma":[0.9961663,0.002686881,0.0001473495,0.0003636368,0.0004799135,0.0001559023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001081121,0.0000605875,0.00181118,0.00004130342,0.00008527145,0.00009531243,0.0002257579,0.841712,0.0001385344,0.1185358,0.003182564,0.03400358],"study_design_scores_gemma":[0.0000120643,0.000003893955,0.00005713739,0.000006383282,0.000008553367,0.000007571571,0.00001644161,0.9706357,0.00006803362,0.02812433,0.00105379,0.000006218916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02469669,0.0002441151,0.9665468,0.0005882874,0.00005651263,0.00006772648,0.0005138554,0.001713274,0.005572681],"genre_scores_gemma":[0.3360456,0.0004629847,0.6500257,0.0002761855,0.00007009769,0.0002436745,0.001073755,0.000912546,0.01088937],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06606711,"threshold_uncertainty_score":0.1313651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03024342604224369,"score_gpt":0.2597798833073858,"score_spread":0.2295364572651421,"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."}}