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| 贝叶斯泊松回归× | 零膨胀模型× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1989 (GLM foundation); Bayesian treatment formalized in 1990s–2000s | 1992 |
| 提出者≠ | Gelman et al. (BDA); classical Poisson GLM from McCullagh & Nelder (1989) | Diane Lambert |
| 类型≠ | Bayesian generalized linear model for count data | Count regression with excess zeros |
| 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 | Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14. DOI ↗ |
| 别名 | Bayesian log-linear count model, Bayesian GLM Poisson, Poisson regression with priors, Bayesian count regression | ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial |
| 相关 | 6 | 6 |
| 摘要≠ | Bayesian Poisson regression models non-negative integer count outcomes using a Poisson likelihood with a log link, placing prior distributions on the regression coefficients. Posterior inference — combining prior beliefs with the data likelihood — produces full probability distributions over the coefficients rather than single-point estimates, enabling coherent uncertainty quantification and incorporation of domain knowledge. | A zero-inflated model is a two-component mixture regression designed for count outcomes that contain more zero values than a standard Poisson or negative binomial distribution can accommodate. One component is a binary process that generates structural zeros; the other is a count process that generates both zeros and positive counts. |
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