方法对比
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| 贝叶斯序数逻辑回归× | 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≠ | 1999 | 1989 (GLM); 1995 (Bayesian BDA) |
| 提出者≠ | Johnson & Albert (1999); Bayesian proportional odds framework | McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al. |
| 类型≠ | Bayesian generalized linear model | Bayesian regression model |
| 开创性文献≠ | Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484 | 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 proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model | Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM |
| 相关 | 6 | 6 |
| 摘要≠ | Bayesian ordinal logistic regression extends the classical proportional odds model by placing prior distributions on the regression coefficients and threshold parameters and updating them with observed data via Bayes' theorem. The result is a full posterior distribution over all parameters, enabling uncertainty quantification without relying on large-sample approximations. | 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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