方法对比
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| Bayesian Regression× | 嵌套 Logit 离散选择模型× | |
|---|---|---|
| 领域≠ | 贝叶斯 | 计量经济学 |
| 方法族≠ | Bayesian methods | Regression model |
| 起源年份≠ | — | 1985 |
| 提出者≠ | — | Daniel McFadden; Ben-Akiva & Lerman |
| 类型≠ | Bayesian linear model | Discrete choice regression model |
| 开创性文献≠ | 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 | Ben-Akiva, M., & Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press. ISBN: 978-0-262-02217-0 |
| 别名≠ | bayesian linear regression, probabilistic regression, bayesian regresyon | Tree Logit Model, Hierarchical Logit Model, Generalized Extreme Value Logit, İç İçe Logit Modeli |
| 相关≠ | 2 | 3 |
| 摘要≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | The Nested Logit model is a discrete choice framework that groups mutually exclusive alternatives into hierarchical nests, allowing correlated unobserved utilities within each nest while maintaining independence across nests. Introduced formally by Ben-Akiva and Lerman (1985) and grounded in McFadden's Generalized Extreme Value (GEV) theory, it extends the standard Multinomial Logit by relaxing the restrictive Independence of Irrelevant Alternatives assumption within predefined groups of similar alternatives. |
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