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| 贝叶斯零膨胀模型× | 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≠ | 1992–2006 | 1989 (GLM); 1995 (Bayesian BDA) |
| 提出者≠ | Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006) | McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al. |
| 类型≠ | Bayesian count regression | Bayesian regression model |
| 开创性文献≠ | Ghosh, S. K., Mukhopadhyay, P., & Lu, J.-C. (2006). Bayesian analysis of zero-inflated regression models. Journal of Statistical Planning and Inference, 136(4), 1360–1375. DOI ↗ | 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 ZIP, Bayesian ZINB, Bayesian zero-inflated Poisson, Bayesian zero-inflated negative binomial | Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM |
| 相关≠ | 5 | 6 |
| 摘要≠ | The Bayesian zero-inflated model handles count data with excess zeros by combining a binary component — identifying structural zeros — with a count component (Poisson or negative binomial) for the remaining counts. Bayesian inference via MCMC provides full posterior distributions for all parameters, enabling principled uncertainty quantification and regularisation through priors. | 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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