{"id":"W2118641598","doi":"10.1080/10920277.2001.10595987","title":"Actuarial Modeling with MCMC and BUGs","year":2001,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Markov chain Monte Carlo; Gibbs sampling; Computer science; Bayesian probability; Suite; Software; Documentation; Bayesian inference; Variety (cybernetics); Inference; Econometrics; Data mining; Artificial intelligence; Mathematics; Programming language","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":[0.01006344,0.0009740212,0.001180848,0.002592146,0.0009600679,0.002479929,0.002341559,0.00150877,0.01091401],"category_scores_gemma":[0.05418802,0.001270015,0.0012905,0.001907118,0.001889976,0.002317475,0.002096405,0.003137292,0.001436639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001249662,"about_ca_system_score_gemma":0.001518124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009313009,"about_ca_topic_score_gemma":0.007794036,"domain_scores_codex":[0.9944642,0.003761888,0.0002288025,0.0003813315,0.001001675,0.0001621574],"domain_scores_gemma":[0.9674696,0.02722323,0.001371166,0.002428962,0.001248029,0.0002590334],"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":[0.00005924751,0.00004259246,0.002497075,0.0001233615,0.0001397308,0.000118685,0.0001815752,0.5092345,0.0002764896,0.4110025,0.006118082,0.07020617],"study_design_scores_gemma":[0.00001520316,0.000007018791,0.0001839332,0.00003071576,0.00001386339,0.00002750058,0.000009275475,0.8645296,0.0001782633,0.1315717,0.003419226,0.00001358752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002406568,0.000199674,0.9943124,0.0003542289,0.00004334865,0.00002592117,0.0001187217,0.0009617134,0.001577362],"genre_scores_gemma":[0.1628917,0.0005461819,0.8316856,0.0002871205,0.000155644,0.0003797657,0.0004143476,0.0007749001,0.002864828],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01091401,"threshold_uncertainty_score":0.05322117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0158531014985217,"score_gpt":0.2746780741681106,"score_spread":0.2588249726695889,"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."}}