{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008862363,0.0002700288,0.0004124691,0.0002711144,0.001406403,0.0005388652,0.0004316027,0.00006000217,0.0001555173],"category_scores_gemma":[0.0001177771,0.0002262996,0.0001324331,0.0008632874,0.0009000721,0.0005794371,0.00006620252,0.0005675635,0.00001763138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001357438,"about_ca_system_score_gemma":0.0003353206,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008471916,"about_ca_topic_score_gemma":0.02152838,"domain_scores_codex":[0.9970163,0.0003020977,0.0004273856,0.0003761119,0.00106504,0.0008131324],"domain_scores_gemma":[0.9985566,0.00008459838,0.0004042413,0.0002360851,0.0002210867,0.0004973835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001465383,0.0002457469,0.7148693,0.000009058717,0.0004548121,0.0004244794,0.01279035,0.003826508,0.00001582922,0.002511607,0.001210957,0.262176],"study_design_scores_gemma":[0.01036754,0.002805809,0.6816826,0.0001500172,0.001055314,0.0006859989,0.03812345,0.009264754,0.000009013876,0.003920601,0.2484441,0.003490843],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682748,0.0000563284,0.0175001,0.001159746,0.0008980352,0.0003059234,0.00000577606,0.00009727571,0.01170197],"genre_scores_gemma":[0.9917639,0.001515256,0.002786839,0.0005554817,0.003232708,0.000008880636,0.000003605657,0.00003194376,0.0001013376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2586852,"threshold_uncertainty_score":0.9998936,"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."}}