方法对比
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| 贝叶斯生存回归× | 贝叶斯广义线性模型× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1990s–2001 | 1989 (GLM); 1995 (Bayesian BDA) |
| 提出者≠ | Ibrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al. | McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al. |
| 类型≠ | Bayesian parametric/semiparametric regression | Bayesian regression model |
| 开创性文献≠ | Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772 | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| 别名 | Bayesian time-to-event regression, Bayesian parametric survival model, Bayesian survival analysis, Bayesian accelerated failure time model | Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM |
| 相关≠ | 5 | 6 |
| 摘要≠ | Bayesian Survival Regression combines parametric or semiparametric survival models — such as Weibull, log-normal, or Cox proportional hazards — with Bayesian inference. Instead of point estimates, it produces full posterior distributions for regression coefficients and the baseline hazard, naturally handling censored observations and incorporating prior knowledge about event times or covariate effects. | A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome. |
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