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| 贝叶斯序数逻辑回归× | 贝叶斯逻辑回归× | |
|---|---|---|
| 领域≠ | 统计学 | 贝叶斯 |
| 方法族≠ | Regression model | Bayesian methods |
| 起源年份≠ | 1999 | 2008 |
| 提出者≠ | Johnson & Albert (1999); Bayesian proportional odds framework | Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008) |
| 类型≠ | Bayesian generalized linear model | Bayesian classification model |
| 开创性文献≠ | Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484 | Gelman, A., Jakulin, A., Pittau, M. G. & Su, Y.-S. (2008). A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models. Annals of Applied Statistics, 2(4), 1360–1383. DOI ↗ |
| 别名≠ | Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model | bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon |
| 相关≠ | 6 | 3 |
| 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. | Bayesian logistic regression is a classification model that applies Bayesian inference to a logistic (sigmoid) likelihood for binary or multinomial outcomes. Developed within the weakly-informative prior framework formalised by Gelman, Jakulin, Pittau and Su (2008), it places a prior distribution over the coefficients and combines that prior with the data likelihood to yield a full posterior distribution for each parameter — delivering calibrated class probabilities and honest uncertainty even in small samples, rare-event settings, or cases of complete separation where frequentist maximum likelihood estimation collapses. |
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