So sánh phương pháp

	Mô hình Bayes lạm phát số không (Bayesian Zero-Inflated Model)	Hồi quy Poisson Bayes
Lĩnh vực	Thống kê	Thống kê
Họ	Regression model	Regression model
Năm ra đời	1992–2006	1989 (GLM foundation); Bayesian treatment formalized in 1990s–2000s
Người khởi xướng	Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006)	Gelman et al. (BDA); classical Poisson GLM from McCullagh & Nelder (1989)
Loại	Bayesian count regression	Bayesian generalized linear model for count data
Công trình gốc	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.	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
Tên gọi khác	Bayesian ZIP, Bayesian ZINB, Bayesian zero-inflated Poisson, Bayesian zero-inflated negative binomial	Bayesian log-linear count model, Bayesian GLM Poisson, Poisson regression with priors, Bayesian count regression
Liên quan	5	6
Tóm tắt	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.	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.